Systems, methods, and devices for developing patient-specific spinal implants, treatments, operations, and/or procedures

ABSTRACT

The disclosure herein relates to systems, methods, and devices for developing patient-specific spinal implants, treatments, operations, and/or procedures. In some embodiments, systems, methods, and devices described herein can comprise using artificial intelligence, machine learning, and/or predictive modeling to predict the outcome of a spinal surgery, one or more parameters of a spine of a patient after spinal surgery, for example after implantation of a spinal rod which can be patient-specific, and/or one or more parameters of one or more recommended patient-specific spinal rods. Furthermore, in some embodiments, systems, methods, and devices described herein can comprise intraoperative tracking for tracking and/or suggesting improvements during spinal surgery based on a pre-operatively determined surgical plan, for example in real-time or substantially real-time. In addition, in some embodiments, systems, methods, and devices described herein can comprise screw planning prior to spinal surgery.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit under 35 U.S.C. § 119(c) ofU.S. Provisional Patent Application No. 62/828,337, filed Apr. 2, 2019,U.S. Provisional Patent Application No. 62/932,727, filed Nov. 8, 2019,U.S. Provisional Patent Application No. 62/828,741, filed Apr. 3, 2019,U.S. Provisional Patent Application No. 62/932,743, filed Nov. 8, 2019,U.S. Provisional Patent Application No. 62/828,326, filed Apr. 2, 2019,U.S. Provisional Patent Application No. 62/932,701, filed Nov. 8, 2019,U.S. Provisional Patent Application No. 62/939,144, filed Nov. 22, 2019,and U.S. Provisional Patent Application No. 62/952,647, filed Dec. 23,2019, each of which is incorporated herein by reference in its entiretyunder 37 C.F.R. § 1.57. Any and all applications for which a foreign ordomestic priority claim is identified in the Application Data Sheet asfiled with the present application are hereby incorporated by referenceunder 37 C.F.R. § 1.57.

BACKGROUND Field

The present application relates to developing patient-specific spinalimplants, surgical plans, treatments, operations, and/or procedures.

Description

Spinal surgery is one of the most frequently performed surgicalprocedures worldwide. Generally speaking, spinal surgery may involveimplantation of one or more spinal implants, such as a spinal rod, tocorrect the curvature of the spine of a patient and to prevent furtherdeterioration. As such, the particular curvature of the spinal rod canbe a key factor in obtaining successful results from surgery.

SUMMARY

Various embodiments described herein relate to systems, methods, anddevices for developing patient-specific spinal implants, surgical plans,treatments, operations, and/or procedures. In some embodiments, systems,methods, and devices described herein for developing patient-specificspinal implants, surgical plans, treatments, operations, and/orprocedures can comprise an iterative virtuous cycle. In someembodiments, the iterative virtuous cycle can further comprisepreoperative, intraoperative, and postoperative techniques or processes.For example, the iterative virtuous cycle can comprise imaging analysis,case planning/simulation, implant production, case support, datacollection, machine learning, and/or predictive modeling. One or moretechniques or processes of the iterative virtuous cycle can be repeated.Further, in some embodiments, systems, methods, and devices describedherein can comprise using artificial intelligence, machine learning,and/or predictive modeling to predict the outcome of a spinal surgery,one or more parameters of a spine of a patient after spinal surgery, forexample after implantation of a spinal rod which can bepatient-specific, and/or one or more parameters of one or morerecommended patient-specific spinal rods. Furthermore, in someembodiments, systems, methods, and devices described herein can compriseintraoperative tracking for tracking and/or suggesting improvementsduring spinal surgery based on a pre-operatively determined surgicalplan, for example in real-time or substantially real-time. In addition,in some embodiments, systems, methods, and devices described herein cancomprise preoperatively determining and/or planning one or more implantsand/or screws prior to spinal surgery, which can comprise screw and/orother spinal implant planning/selection.

In particular, in some embodiments, a computer-implemented method forgenerating and assisting patient-specific spinal treatment comprises:analyzing, using a computer system, one or more preoperative medicalimages of a spine of a patient to determine one or more preoperativespinopelvic parameters, wherein the one or more spinopelvic parameterscomprise one or more of lumbar lordosis (LL), preoperative thoracickyphosis (TK), pelvic incidence (PI), pelvic tilt (PT), or sagittalvertical axis (SVA) for one or more vertebrae; transforming, using thecomputer system, the determined one or more preoperative spinopelvicparameters to obtain one or more preoperative spinopelvic parameters ina frequency domain, wherein the transforming comprises applying aFourier transformation to the determined one or more preoperativespinopelvic parameters; filtering, using the computer system, the one ormore preoperative spinopelvic parameters in the frequency domain,wherein the filtering comprises filtering out one or more of the one ormore preoperative spinopelvic parameters in the frequency domaincomprising a frequency level above a predetermined threshold; applying,using the computer system, one or more predictive models to generate apredicted surgical outcome in the frequency domain based at least inpart on the filtered one or more preoperative spinopelvic parameters inthe frequency domain and the one or more preoperative non-imaging dataof the subject, wherein the one or more predictive models comprises oneor more of a generative adversarial network (GAN) algorithm,convolutional neural network (CNN) algorithm, or recurrent neuralnetwork (RNN) algorithm; and transforming, using the computer system,the generated predicted surgical outcome in the frequency domain toobtain a generated predictive surgical outcome in a spatial domain,wherein the transforming the generated predicted surgical outcome in thefrequency domain comprises applying an inverse Fourier transformation tothe generated predicted surgical outcome in the frequency domain,generating, using the computer system, a patient-specific spinaltreatment based at least in part on the generated predictive surgicaloutcome in the spatial domain, wherein the generated patient-specificspinal treatment comprises one or more patient-specific spinal surgicalprocedures; attaching one or more intraoperative tracking modules to oneor more vertebral implants for implanting to one or more vertebrae ofinterest during spinal surgery of the patient, wherein the one or moreintraoperative tracking modules comprise a strip for blocking a powercircuit within the one or more intraoperative tracking modules; removingthe strip from one or more intraoperative tracking modules to initiatetracking of one or more angles between the one or more vertebrae towhich the one or more intraoperative tracking modules are attached to;and generating, by the computer system, intraoperative tracking data inreal-time and comparing the generated tracking data in real-time to thegenerated one or more patient-specific spinal surgical procedures toassist the generated patient-specific spinal treatment, wherein thecomputer system comprises a computer processor and an electronic storagemedium.

In some embodiments of a computer-implemented method for generating andassisting patient-specific spinal treatment, the one or more vertebralimplants comprise one or more vertebral screws. In some embodiments, theone or more vertebral screws comprise one or more tulip screws. In someembodiments, the one or more intraoperative tracking modules comprisesone or more notches configured to attach or remove the one or moreintraoperative tracking modules to the one or more vertebral screws. Insome embodiments, the one or more intraoperative tracking modulescomprises a first conduit adapted to allow insertion of a surgical tool,and wherein the one or more intraoperative tracking modules comprises asecond conduit adapted to allow insertion of a spinal rod. In someembodiments, a longitudinal axis of the first conduit is substantiallyperpendicular to a longitudinal axis of the second conduit. In someembodiments, the second conduit comprises a top section and a bottomsection, wherein a width of the top section is larger than a width ofthe bottom section. In some embodiments, the second conduit is formed bytwo notches of the one or more intraoperative tracking modules, whereinthe two notches are adapted to attach to a horizontal notch of the oneor more vertebral screws.

In some embodiments of a computer-implemented method for generating andassisting patient-specific spinal treatment, the one or more spinopelvicparameters are determined automatically by the computer system. In someembodiments, the one or more preoperative medical images of the spine ofthe patient comprise one or more sagittal x-ray images and one or morefrontal x-ray images. In some embodiments, the generated predictivesurgical outcome in the spatial domain comprises one or morepostoperative spinopelvic parameters. In some embodiments, the generatedpatient-specific spinal treatment further comprises one or morespecifications of a spinal rod to be implanted to the spine of thepatient.

In some embodiments, a computer-implemented method of predicting asurgical outcome a spinal surgery of a subject comprises: inputting,into a computer system, one or more preoperative inputs relating to thesubject, wherein the one or more preoperative inputs comprise one ormore preoperative medical images of a spine of the subject and one ormore preoperative non-imaging data of the subject; determining, usingthe computer system, one or more measurements from the inputted one ormore preoperative medical images of the spine of the subject, whereinthe one or more measurements comprise a position of one or morevertebrae of the spine of the subject; determining, using the computersystem, one or more preoperative spinopelvic parameters based at leastin part on the one or more determined measurements, wherein the one ormore preoperative spinopelvic parameters comprise one or more of lumbarlordosis (LL), preoperative thoracic kyphosis (TK), pelvic incidence(PI), pelvic tilt (PT), or sagittal vertical axis (SVA) for one or morevertebrae; transforming, using the computer system, the determined oneor more preoperative spinopelvic parameters to obtain one or morepreoperative spinopelvic parameters in a frequency domain, wherein thetransforming comprises applying a Fourier transformation to thedetermined one or more preoperative spinopelvic parameters; filtering,using the computer system, the one or more preoperative spinopelvicparameters in the frequency domain, wherein the filtering comprisesfiltering out one or more of the one or more preoperative spinopelvicparameters in the frequency domain comprising a frequency level above apredetermined threshold; applying, using the computer system, one ormore predictive models to generate a predicted surgical outcome in thefrequency domain based at least in part on the filtered one or morepreoperative spinopelvic parameters in the frequency domain and the oneor more preoperative non-imaging data of the subject; and transforming,using the computer system, the generated predicted surgical outcome inthe frequency domain to obtain a generated predictive surgical outcomein a spatial domain, wherein the transforming the generated predictedsurgical outcome in the frequency domain comprises applying an inverseFourier transformation to the generated predicted surgical outcome inthe frequency domain, wherein the computer system comprises a computerprocessor and an electronic storage medium.

In some embodiments of a computer-implemented method of predicting asurgical outcome a spinal surgery of a subject, the one or morepredictive models comprises one or more of a generative adversarialnetwork (GAN) algorithm, convolutional neural network (CNN) algorithm,or recurrent neural network (RNN) algorithm. In some embodiments, acomputer-implemented method of predicting a surgical outcome a spinalsurgery of a subject further comprises generating, by the computersystem, a preoperatively determined spinal surgical plan for the subjectbased at least in part on the generated predictive surgical outcome inthe spatial domain. In some embodiments, the generated preoperativelydetermined spinal surgical plan comprises one or more specifications ofa spinal rod for implantation during the spinal surgery of the subject.

In some embodiments of a computer-implemented method of predicting asurgical outcome a spinal surgery of a subject, the one or moremeasurements from the inputted one or more preoperative medical imagesof the spine of the subject are determined automatically by the computersystem. In some embodiments, the inputted one or more preoperativemedical images of the spine of the subject comprises one or moresagittal x-ray images and one or more frontal x-ray images. In someembodiments, the generated predictive surgical outcome in the spatialdomain comprises one or more of one or more postoperative spinopelvicparameters or one or more specifications of a spinal rod to be implantedto the spine of the subject. In some embodiments, the one or morepreoperative inputs further comprise one or more specifications of aspinal rod proposed to be implanted to the spine of the subject.

In some embodiments, a computer-implemented method of training apredictive model for predicting a surgical outcome a spinal surgery of asubject comprises: inputting, into a computer system, one or morepreoperative inputs and one or more postoperative inputs relating to oneor more previous subjects, wherein each of the one or more preoperativeinputs and the one or more postoperative inputs relating to one or moreprevious subjects comprise one or more preoperative medical images andone or more postoperative medical images of a spine of the one or moreprevious subjects and one or more preoperative non-imaging data and oneor more postoperative non-imaging of the one or more previous subjects;determining, using the computer system, one or more measurements fromthe inputted one or more preoperative medical images and one or morepostoperative medical images of the spine of the one or more previoussubjects, wherein the one or more measurements comprise a position ofone or more vertebrae of the spine of the one or more previous subjects;determining, using the computer system, one or more preoperativespinopelvic parameters and one or more postoperative spinopelvicparameters of the spine of the one or more previous subjects based atleast in part on the one or more determined measurements, wherein theone or more preoperative spinopelvic parameters and the one or morepostoperative spinopelvic parameters of the one or more previoussubjects comprise one or more of lumbar lordosis (LL), preoperativethoracic kyphosis (TK), pelvic incidence (PI), pelvic tilt (PT), orsagittal vertical axis (SVA) for one or more vertebrae; applying, usingthe computer system, a data compression technique to the determined oneor more preoperative spinopelvic parameters and the one or morepostoperative spinopelvic parameters of the one or more previoussubjects to obtain compressed one or more preoperative spinopelvicparameters and one or more postoperative spinopelvic parameters of theone or more previous subjects; filtering, using the computer system, thecompressed one or more preoperative spinopelvic parameters and the oneor more postoperative spinopelvic parameters of the one or more previoussubjects, wherein the filtering comprises filtering out one or morecompressed preoperative spinopelvic parameters and one or morepostoperative spinopelvic parameters of the one or more previoussubjects comprising a noise level above a predetermined threshold;training, using the computer system, one or more predictive models basedat least in part on the filtered compressed one or more preoperativespinopelvic parameters and one or more postoperative spinopelvicparameters of the one or more previous subjects, the one or morepreoperative non-imaging data of the one or more previous subjects, andthe one or more postoperative non-imaging of the one or more previoussubjects; and testing, using the computer system, the trained one ormore predicted models on one or more test preoperative inputs and one ormore test postoperative inputs relating to one or more test subjects,wherein each of the one or more test preoperative inputs and the one ormore test postoperative inputs relating to one or more test subjectscomprise one or more test preoperative medical images and one or moretest postoperative medical images of a spine of the one or more testsubjects, wherein the one or more test subjects are separate from theone or more previous subjects, wherein the trained and tested one ormore predictive models are configured to predict the surgical outcome ofthe spinal surgery of the subject based at least in part on one or morespinopelvic parameters derived from one or more preoperative medicalimages of a spine of the subject, wherein the computer system comprisesa computer processor and an electronic storage medium.

In some embodiments of a computer-implemented method of training apredictive model for predicting a surgical outcome a spinal surgery of asubject, the data compression technique comprises a Fouriertransformation. In some embodiments, the data compression techniquecomprises a polynomial function. In some embodiments, the training ofthe one or more predictive models is based at least in part on one ormore of a generative adversarial network (GAN) algorithm, convolutionalneural network (CNN) algorithm, or recurrent neural network (RNN)algorithm.

In some embodiments, a computer-implemented method of training apredictive model for predicting a surgical outcome a spinal surgery of asubject further comprises generating, using the computer system, one ormore augmented measurements by applying a Gaussian process to thedetermined one or more measurements from the inputted one or morepreoperative medical images of the spine of the previous subjects,wherein the generated one or more augmented measurements are configuredto be used to train the one or more predictive models.

In some embodiments, a computer-implemented method of training apredictive model for predicting a surgical outcome a spinal surgery of asubject further comprises generating, using the computer system, one ormore augmented measurements from rotating the one or more preoperativemedical images and the one or more postoperative medical images of thespine of the one or more previous subjects along a vertical axis,wherein the generated one or more augmented measurements are configuredto be used to train the one or more predictive models. In someembodiments, the one or more preoperative medical images and the one ormore postoperative medical images of the spine of the one or moreprevious subjects are rotated along the vertical axis in 180 degrees.

In some embodiments of a computer-implemented method of training apredictive model for predicting a surgical outcome a spinal surgery of asubject, the one or more postoperative inputs relating to one or moreprevious subjects further comprise one or more specifications of aspinal rod implanted to the spine of the one or more previous subjects.

For purposes of this summary, certain aspects, advantages, and novelfeatures of the invention are described herein. It is to be understoodthat not necessarily all such advantages may be achieved in accordancewith any particular embodiment of the invention. Thus, for example,those skilled in the art will recognize that the invention may beembodied or carried out in a manner that achieves one advantage or groupof advantages as taught herein without necessarily achieving otheradvantages as may be taught or suggested herein.

All of these embodiments are intended to be within the scope of theinvention herein disclosed. These and other embodiments will becomereadily apparent to those skilled in the art from the following detaileddescription having reference to the attached figures, the invention notbeing limited to any particular disclosed embodiment(s).

BRIEF DESCRIPTION OF THE DRAWINGS

A better understanding of the devices and methods described herein willbe appreciated upon reference to the following description inconjunction with the accompanying drawings, wherein:

FIG. 1 is a flowchart illustrating an overview of an exampleembodiment(s) of an iterative virtuous cycle for developingpatient-specific spinal implants, treatments, operations, and/orprocedures;

FIG. 2 illustrates an example embodiment(s) for developing spinalimplants, treatments, operations, and/or procedures that comprises asoftware platform;

FIG. 3 is a flowchart illustrating an example embodiment(s) ofpredictive modeling that can be used for developing patient-specificimplants, treatments, operations, and/or procedures;

FIG. 4 is a flowchart illustrating an example embodiment(s) ofpredictive modeling and its role in developing patient-specificimplants, treatments, operations, and/or procedures;

FIG. 5 is a flowchart illustrating an example embodiment(s) ofpredictive modeling and its role in developing patient-specificimplants, treatments, operations, and/or procedures;

FIG. 6 is a flowchart illustrating an example embodiment(s) and/oroverview of machine learning in developing patient-specific implants,treatments, operations, and/or procedures;

FIG. 7 is a flowchart illustrating an example embodiment(s) ofpredictive modeling in developing patient-specific implants, treatments,operations, and/or procedures;

FIG. 8 is a flowchart illustrating an example embodiment(s) of implantproduction, case support, data collection, and/or intraoperativetracking during spinal surgery for developing patient-specific spinalimplants, treatments, operations, and/or procedures;

FIG. 9 is a schematic diagram illustrating an example embodiment(s) ofintraoperative tracking;

FIGS. 10A-10D are schematic diagrams illustrating an exampleembodiment(s) of an intraoperative tracking module and compatibilitythereof;

FIGS. 11A-11E illustrate an example embodiment(s) of an intraoperativetracking module and compatibility thereof;

FIGS. 12A-12E illustrate an example embodiment(s) of an intraoperativetracking module and compatibility thereof;

FIGS. 13A-13G illustrate an example embodiment(s) of an intraoperativetracking module and compatibility thereof;

FIGS. 14A-14F illustrate an example embodiment(s) of an intraoperativetracking module and compatibility thereof;

FIG. 15 is a flowchart illustrating an example embodiment(s) ofintraoperative tracking and its role in developing patient-specificimplants, treatments, operations, and/or procedures;

FIG. 16 is a schematic diagram illustrating an example embodiment(s) ofpositioning one or more spinal screws with an intraoperative trackingmodule and one or more spinal screws without an intraoperative trackingmodule on a spine during surgery;

FIG. 17 is a flowchart illustrating an example embodiment(s) of rodplacement and intraoperative tracking;

FIG. 18A-18D are screenshots of an example embodiment(s) of a softwareplatform for assisting rod placement and intraoperative tracking;

FIG. 19 is a schematic diagram illustrating an example embodiment(s) ofpositioning a rod during spinal surgery based on intraoperativetracking;

FIG. 20 is a flowchart and/or schematic diagram illustrating an exampleembodiment(s) of calculating screw offset for intraoperative tracking;

FIG. 21 is a flowchart and/or schematic diagram illustrating an exampleembodiment(s) of discarding intraoperative tracking modules and/or nutsafter intraoperative tracking and/or finalization of rod placement;

FIG. 22 illustrates an example embodiment(s) of intra-operative trackingthat can be used in conjunction with PediGuard technology;

FIGS. 23A-23B illustrates an example embodiment(s) of intra-operativetracking that can be used in conjunction with Choker technology;

FIG. 24 illustrates an example embodiment(s) of intra-operative trackingthat can be used in conjunction with a surgical robot(s);

FIG. 25 illustrates an example embodiment(s) of intra-operative trackingthat can be used in conjunction with a surgical robot(s);

FIGS. 26A-26B illustrate an example(s) of a preoperative spinal x-rayimage(s) that can be used for one or more embodiments described herein;

FIGS. 27A-27C illustrate an example(s) of a preoperative sagittal spinalx-ray image(s) that can be used for one or more embodiments describedherein;

FIGS. 28A-28D illustrate an example(s) of a preoperative coronal spinalx-ray image(s) that can be used for one or more embodiments describedherein;

FIGS. 29A-29E illustrate an example(s) of a postoperative sagittalspinal x-ray image(s) that can be used for one or more embodimentsdescribed herein;

FIGS. 30A-30B illustrate an example(s) of a postoperative coronal spinalx-ray image(s) that can be used for one or more embodiments describedherein;

FIG. 31 illustrates an example(s) of a postoperative and/orintraoperative CT scan that can be used for one or more embodimentsdescribed herein;

FIGS. 32A-32G illustrate an example embodiment(s) of a screw planningmemo(s);

FIGS. 33A-33K illustrate an example embodiment(s) of a screw planningmemo(s);

FIG. 34 is a schematic diagram illustrating an embodiment(s) of a systemfor developing patient-specific spinal implants, treatments, operations,and/or procedures; and

FIG. 35 is a block diagram depicting an embodiment(s) of a computerhardware system configured to run software for implementing one or moreembodiments of a system for developing patient-specific spinal implants,treatments, operations, and/or procedures.

DETAILED DESCRIPTION

Although several embodiments, examples, and illustrations are disclosedbelow, it will be understood by those of ordinary skill in the art thatthe inventions described herein extend beyond the specifically disclosedembodiments, examples, and illustrations and includes other uses of theinventions and obvious modifications and equivalents thereof.Embodiments of the inventions are described with reference to theaccompanying figures, wherein like numerals refer to like elementsthroughout. The terminology used in the description presented herein isnot intended to be interpreted in any limited or restrictive mannersimply because it is being used in conjunction with a detaileddescription of certain specific embodiments of the inventions. Inaddition, embodiments of the inventions can comprise several novelfeatures and no single feature is solely responsible for its desirableattributes or is essential to practicing the inventions hereindescribed.

Spinal surgery is one of the most frequently performed surgicalprocedures worldwide. Generally speaking, spinal surgery may involveimplantation of one or more implants, such as a spinal rod(s), cage(s),and/or one or more screw(s) to correct the curvature of the spine of apatient and to prevent further deterioration. As such, correspondencebetween one or more spinal implants and patient anatomy can be a keyfactor in obtaining successful results from surgery. In particular, theparticular curvature, dimensions, shape and/or size of one or morespinal rods, cages, and/or screws can be crucial to obtain successfulsurgical results.

Various embodiments described herein relate to systems, methods, anddevices for developing patient-specific spinal implants, treatments,operations, and/or procedures. In some embodiments, systems, methods,and devices described herein for developing patient-specific spinalimplants, surgical plans, treatments, operations, and/or procedures cancomprise an iterative virtuous cycle. The iterative virtuous cycle canfurther comprise one or more preoperative, intraoperative, andpostoperative techniques or processes. For example, the iterativevirtuous cycle can comprise one or more of imaging analysis, casesimulation, implant production, case support, data collection, machinelearning, and/or predictive modeling. One or more techniques orprocesses of the iterative virtuous cycle can be repeated.

In particular, there can be a desired surgical outcome that isparticular to each patient. For example, based on the current state of aspine of a patient, it can be known from past data, experience, and/orliterature, that a particular patient's spine should be corrected in acertain way and/or degree. In turn, in order to obtain such correctiveresults, it can be advantageous to design, generate, and/or otherformulate specific dimensions and/or other variables pertaining to oneor more implants that are specific to the particular patient. Forexample, there can be one or more desirable variables and/or parametersfor one or more spinal rods, cages, and/or screws for implantation for aspecific patient. As such, some systems, devices, and methods describedherein are configured to utilize one or more medical images of a spineof a patient and/or one or more parameters of the spine of the patientand analyze the same to determine one or more desired parameters and/orvariables of one or more spinal rods, cages, and/or screws forimplantation. Based on the determined one or more desired parametersand/or variables, some systems, devices, and methods described hereincan be further configured to manufacture, produce, modify, select,provide guidance for selection of, and/or generate instructions tomanufacture, produce, modify, and/or select one or more spinal rods,cages, and/or screws that are specifically customized for a particularpatient. In particular, in some embodiments, the systems, methods, anddevices described herein can utilize predictive modeling, machinelearning, and/or artificial intelligence as part of developingpatient-specific spinal implants, surgical plans, treatments,operations, and/or procedures

In addition to designing, producing, and/or otherwise obtaining an idealor desired patient-specific spinal implant, it can be equally, if notmore, important that such implant is correctly implanted according to adesired and/or predetermined surgical plan. In other words, even if oneor more spinal rods, cages, and/or screws are produced, selected, orotherwise obtained for a specific patient, its effects can be limited ifthe implantation or other surgical procedure is not conducted accordingto a desired or predetermined plan. As such, it can be advantageous tobe able to ensure or at least increase the chances that surgery or aprocedure thereof is performed as desired. To such effect, some systems,devices, and methods described herein provide intraoperative tracking toprovide guidance and/or performance evaluation during spinal surgery,for example in real-time or in substantially real-time.

Further, it can be advantageous to be able to analyze data relating tospecific patient spinal conditions pre-operation and/or post-operationand utilize the same in order to predict the outcome of spinal surgeryfor a new patient. In some embodiments, predictive analysis can also beused in generating a patient-specific surgical plan, which can compriseone or more parameters and/or variables for one or more spinal rods,cages, and/or screws. Accordingly, somed systems, methods, and devicesdisclosed herein are configured to utilize predictive modeling togenerate predictive surgical outcome(s) and/or patient-specific surgicalplan(s).

Iterative Virtuous Cycle

FIG. 1 is a flowchart illustrating an overview of an exampleembodiment(s) of an iterative virtuous cycle for developingpatient-specific spinal implants, treatments, operations, and/orprocedures. As illustrated in FIG. 1 , some embodiments of the systems,methods, and devices described herein comprise one or more processesthat can form an iterative virtuous cycle. For example, an iterativevirtuous cycle can comprise one or more of the following: (1) imaginganalysis 102; (2) case simulation 104; (3) personalized orpatient-specific implant production 106; (4) case support 108; (5) datacollection 110; (6) machine learning 112; and/or (7) predictive modeling114. Certain embodiments may comprise any subset of the aforementionedprocesses. Further, one or more processes or techniques of a virtuousiterative cycle can be repeated.

Some processes or techniques of the virtuous iterative cycle can beperformed at different points in time. For example, in some embodiments,imaging analysis, case simulation, and/or implant production can beperformed pre-operation or prior to surgery. In some embodiments, casesupport and/or data collection may be performed during operation orintra-operation or during surgery. Lastly, in some embodiments, somedata collection, machine learning, and/or predictive modeling can beperformed post-operation or after surgery. In some embodiments, thewhole virtuous iterative cycle and/or portions thereof can be repeatedfor the same and/or different patient in certain embodiments. U.S. Pat.No. 10,292,770 in its entirety is hereby incorporated by reference under37 C.F.R. § 1.57.

FIG. 2 illustrates an example embodiment(s) for developing spinalimplants, treatments, operations, and/or procedures using a softwareplatform. As illustrated in FIG. 2 , in some embodiments, the systems,devices, and methods described herein can comprise and/or utilize asoftware platform. In some embodiments, the software platform cancomprise a user interface 202 that allows users, such as a surgeonand/or other medical personnel, to access the system. In someembodiments, the user interface 202 can allow a user to access and/orconduct preoperative analysis and/or preoperative planning of a spinalsurgery, which can comprise specifying a patient-specific spinal rodand/or developing a patient-specific plan for implantation of a rod. Insome embodiments, the user interface 202 can allow a user to accessand/or conduct postoperative analysis of a spinal surgery, which cancomprise analyzing the results of surgery according to a preoperativelydefined plan. In some embodiments, the user interface 202 can alsocomprise and/or facilitate ordering of one or more patient-specificspinal implants, such as for example spinal rods, cages, screws, and/orthe like.

In some embodiments, the user interface 202 can comprise and/or beconfigured to provide data visualization 204 to a user of the case athand during preoperative planning and/or postoperative analysis. Forexample, in some embodiments, such data visualization 204 can includeone or more representations of one or more parameters of the spine ofthe patient prior to and/or after surgery.

In some embodiments, the user interface 202 can comprise and/or beconfigured to provide one or more predictive modeling aspects orfeatures 206 to a user. As discussed herein, predictive modeling can beused in some embodiments to predict the outcome of spinal surgery basedon one or more of patient characteristics, preoperative spinalparameters, proposed spinal rod specifications, and/or the like.

In some embodiments, the user interface 202 can comprise and/or beconfigured to provide screw planning aspects or features 208 to a user.For example, in some embodiments, the system can provide preoperativescrew planning features to allow a surgeon or other medical personnel toreduce and/or precise the types of screws that will be used duringsurgery, thereby decreasing the size of the screw kit that needs to beprepared and shipped prior to surgery.

In some embodiments, the user interface 202 can comprise and/or beconfigured to provide cage selection support aspects or features 210 toa user. For example, in some embodiments, the system can provide cageselection support features to facilitate selection of a particular typeor range of cages that are desirable for a patient prior to surgery.

Predictive Modeling

In some embodiments, the system is configured to generate and/or utilizeone or more predictive models, machine learning algorithms, and/orartificial intelligence for developing patient-specific implants,surgical plans, treatments, operations, and/or procedures.

In particular, in some embodiments, the system can be configured topredict the surgical outcome and/or the results of a compensatorymechanism(s) and/or spinal curvature or parameters post-surgery based atleast in part on one or more inputs, such as for example one or morepreoperative medical images of a spine of a patient, one or more spinalparameters of the patient prior to surgery, one or more proposedsurgical steps, and/or specifications of a proposed spinal rod forimplantation.

In some embodiments, the system can be configured to predict thesurgical outcome, results of a compensatory mechanism(s) and/or spinalcurvature or parameters post-surgery, and/or specifications of aproposed spinal rod for implantation based at least in part on one ormore inputs, such as for example one or more preoperative medical imagesof a spine of a patient, one or more spinal parameters of the patientprior to surgery, and/or one or more proposed surgical steps.

Further, in some embodiments, the system can be configured to predictthe surgical outcome, results of a compensatory mechanism(s) and/orspinal curvature or parameters post-surgery, specifications of aproposed spinal rod for implantation, and/or one or more proposedsurgical steps based at least in part on one or more inputs, such as forexample one or more preoperative medical images of a spine of a patientand/or one or more spinal parameters of the patient prior to surgery.

In some embodiments, the one or more predictive models and/or algorithmscan be configured to predict one or more surgical parameters and/orvariables that may result from a surgical procedure, for example, of thespine of a patient. In some embodiments, the one or more predictivemodels and/or algorithms can be configured to generate a surgical planfor achieving desired surgical outcome. For example, in someembodiments, the systems, devices, and methods described herein can beconfigured to access preoperative patient input data and generate asurgical plan for implanting a spinal rod into the patient where thegenerated surgical plan that is personalized for the patient isconfigured to generate an optimized post-surgical spine curvature forthe particular patient.

When a patient undergoes surgery by a doctor, the surgical outcomes canbe generally determined based on the surgeon's estimations and/or priorsurgical experience. For example, when a spinal rod is implanted into apatient, the surgeon can analyze the patient's body and othercharacteristics. Based on these observations, the surgeon can provide ageneral estimate and/or select certain surgical parameters that thesurgeon believes will result in a better spinal curvature for thepatient post-surgery. However, in reality, the surgeon's estimations andselected surgical parameters may not result in the most desired oroptimal surgical outcomes.

For example, when performing a spinal surgery for improving a patient'sspinal curvature, the doctor can select a curvature for the spinal rodto be implanted into a patient. The rod curvature selection can bedetermined and/or estimated by the surgeon based on the doctor'sobservations of the patient, and such determinations and estimations mayresult in the patient having a spinal curvature that is less thanoptimal after the surgery. Accordingly, it can be beneficial to have asystem that can predict surgical parameters post-surgery based onpre-operative patient characteristics. For example, it can be helpful todetermine, before performing spinal surgery, one or more optimalsurgical parameters that should be utilized in a surgical plan in orderto achieve the optimal spinal curvature post-surgery for a particularpatient with certain characteristics. In some embodiments describedherein, systems, methods, and devices are configured to address theforegoing issues.

In particular, in some embodiments, the system can be configured toaccess pre-operative patient characteristics and input one or morevariables therefrom into a predictive algorithm. In certain embodiments,the system can be configured to utilize the predictive algorithm togenerate one or more surgical plans having one or more specific surgicalparameters that are predicted to generate an optimal or optimizedpost-surgical outcome for the patient. For example, the system can beconfigured to receive one or more patient characteristics, such aspreoperative spinal curvatures and angles, patient age, genetic mappingor genetic conditions, and/or other variables. In particular, theexistence of certain genes or genetic conditions may have a correlationwith a particular condition, such as scoliosis, and/or surgical outcome.In some embodiments, the system can be configured to utilize suchpatient characteristics and/or variables for inputting into a predictivealgorithm. In some embodiments, the system can be configured to outputbased on the predictive algorithm specific surgical parameters, such asthe optimal or optimized spinal rod curvature and/or instrumentationpositions and/or other variables for achieving the optimal spinalcurvature post-surgery for the patient.

In some embodiments, the system is configured to utilize the one or morepredictive algorithms to generate a predictive post-surgical outcome.For example, the system can be configured to access one or more patientcharacteristics and/or surgical parameters that a surgeon intends to usein a surgical plan. In some embodiments, the system can be configured toutilize the predictive algorithm to determine the post-surgical outcomethat is predicted to result from the surgical parameters associated withthe surgical plan. For example, the system can be configured to accesspatient characteristics, such as preoperative spinal curvature and/orangles, patient age, genetic conditions, and/or any other variable. Thesystem can also be configured to access the curvature of the spinal rodthat the surgeon intends to implant into the patient. In someembodiments, the system can be configured to generate a predictivepost-surgical spinal curvature for the patient based on the inputted ofvariables, in this example, the patient characteristics and thecurvature of the spine rod to be implanted into the patient.

As one of ordinary skill will appreciate, the systems, devices, andmethods disclosed herein can be applied to a myriad of surgicalprocedures and is not intended to be limited to spinal surgeries. Forexample, the systems, devices, and methods disclosed herein can beapplied to any kind of surgery, including but not limited orthopedicsurgeries, such as, for a patient's neck, head, hand, foot, leg, and armsurgeries.

In some embodiments, the system can be configured to generate apredictive model for predicting one or more post-surgical parameters. Insome embodiments, the system can be configured to generate thepredictive model by selecting a dataset comprising one or morepreoperative and/or postoperative data for one or more patients. As anon-limiting example, in some embodiments, the system can be configuredto identify all cases with proximal junctional kyphosis (PJK) and removesuch cases from the dataset. In some embodiments, the system can beconfigured to remove all pediatric cases from the dataset. In someembodiments, removal of the pediatric cases can be based on priorknowledge of the cases in the dataset.

In some embodiments, the system can be configured to split data based oninstrumented levels into different groups. For example, the system canbe configured to split the dataset into a first group wherein there isinstrumentation at L1-L5 and at S1-Iliac, and into a second groupwherein there is instrumentation at T10-T12 and at S1-Iliac. For eachgroup, in some embodiments, the system can be configured to split datainto a training set and a testing set (for example, ˜75% of the data forthe training set and ˜25% of the data for the testing set).

In some embodiments, the system can be configured to select one or moreinput parameters, for example, age, PI pre-op value, PT pre-op value, LLpre-op value, TK pre-op value, SVA pre-op value, lower instrumentedlevel, upper instrumented level, LL post-op target value, surgeon,weight, shape of the preoperative spline, preoperative x-ray, or thelike. In some embodiments, the system can be configured to standardizethe range of input parameters and/or utilize a scaling methodology.

In some embodiments, the system can be configured to standardize thedata based on the training set. In some embodiments, the system can beconfigured to select a first model type from a plurality of model types,such as for example a linear model, neural network, deep learning, SVR,or the like. In some embodiments, the system can be configured to selectthe best model using cross validation. In some embodiments, the systemcan be configured to perform cross validation by splitting the data setinto a new training set and a new testing set. In some embodiments, thesystem can be configured to train the model with the new training setand evaluate the results with the new testing set.

In some embodiments, the system can be configured to repeat the trainingprocess until each data has been once and only once in a testing set. Insome embodiments, the system can be configured to train the modelselected with the training set. In some embodiments, the system can beconfigured to utilize a linear model named least-angle regression (LARS)with regularization and variable selection algorithm least absoluteshrinkage and selection operator (LASSO). In some embodiments, thesystem can be configured to test the trained model with the testing setto determine whether the trained model satisfies an accuracy thresholdlevel. In some embodiments, the system can be configured to utilize thetrained model to compare with a proposed surgical plan to determinewhether the surgical plan is optimal for the patient and/or will produceoptimal post-operative surgical results for the patient having certainpatient characteristics.

FIG. 3 is a flowchart illustrating an example embodiment(s) ofpredictive modeling. In the illustrated example embodiment, the systemcan be configured to access and/or retrieve one or more preoperative,intraoperative, and/or postoperative data sets at block 302. The one ormore datasets can be accessed and/or retrieved from one or moredatabases, such as a plan database 316 and/or operation database 318among others.

In some embodiments, the system can be configured to determine whetherthe retrieved or accessed dataset comprises postoperative data at block304. If a dataset comprises postoperative data, the system can beconfigured to identify one or more variables of interest, such as thosedescribed herein, from the postoperative data and/or relatedpreoperative and/or intraoperative datasets at block 306. In someembodiments, based in part on the identified one or more variables, thesystem can be configured to train a predictive modeling algorithm atblock 308 according to one or more processes or techniques describedherein. In some embodiments, this training process and/or techniqueand/or portion thereof can be repeated as necessary. For example, incertain embodiments, the system can be configured to repeat the trainingalgorithm and/or a portion thereof as additional data becomes available,such as data from an additional patient and/or additional postoperativedata from a known patient or the like.

In some embodiments, if the retrieved or accessed dataset is for a newcase, and as such does not comprise postoperative data the system can beconfigured to apply one or more predictive modeling algorithms to suchinput preoperative data. In particular, in some embodiments, the systemcan be configured to identify one or more variables from the inputpreoperative data and/or compare the same with one or more otherdatasets at block 310. In some embodiments, based on the comparisonand/or other data analysis, the system can be configured to apply one ormore predictive modeling algorithms to the input preoperative data.Subsequently, in some embodiments, the system can be configured togenerate one or more predicted surgical outcomes and/or plan and/or oneor more variables thereof based on the predictive model at block 312. Insome embodiments, based at least in part on the resulting surgical planand/or one or more variables thereof, the system can be configured toproduce, modify, select, and/or provide guidance for selection of one ormore spinal implants at block 314, such as spinal rods, cages, and/orscrews.

Additional Features of Predictive Modeling

In some embodiments, the system is configured to perform acomputer-implemented method that is configured to generate a predictivemodel for determining post-operative parameters, such as for examplethoracic kyphosis and/or pelvic tilt, wherein the computer-implementedmethod can comprise accessing a dataset from an electronic database, thedataset comprising data about the patient (for example, an X-ray imagesor clinical information) and the surgery strategy (for example, upperinstrumented vertebra, lower instrumented vertebra, or the like). Insome embodiments, the computer-implemented method is configured todefine in the dataset which parameters should be inputs of the model andwhich parameters should be outputs of the model. For example, outputs ofthe model can comprise the parameters that the system is configured tobe predicted.

In some embodiments, the system is configured to optionally divide thedataset into a plurality of categories based on the spinal surgerydomain knowledge. For example, the dataset can be configured to separateadult cases and pediatric cases. In some embodiments, the system can beconfigured to generate a predictive model for each category. In someembodiments, the system is configured to separate the data into a firstsubcategory and a second subcategory, wherein the first subcategory isused for training and the second subcategory is for testing thepredictive model. In some embodiments, the system is configured tostandardize the data using the first category.

In some embodiments, the system is configured to select a modelalgorithm, for example, neural network, support vector regression,linear models, or the like. In some embodiments, the system isconfigured to select the model based on using a cross validationstrategy. In some embodiments, the system is configured to input one ormore input values into the model based on the first subcategory to trainthe statistical models based on the output values of the firstsubcategory. In some embodiments, the system is configured to input oneor more input data values in the generated trained model and compare theoutputs generated by the model with the output values of the firstsubcategory. In some embodiments, based on the foregoing comparison, amodel is generated and the performance of the model is known. In someembodiments, the system is configured to store the first trainedstatistical model in a data repository. In some embodiments, the systemcomprises a computer processor and electronic memory. In certainembodiments, one or more of the above-identified processes or techniquesare repeated for each of the categories defined by when dividing thedataset based on a spinal surgery domain knowledge block as describedabove.

In some embodiments, the system is configured to perform acomputer-implemented method for generating a predictive model forestimating post-operative parameters, wherein the computer-implementedmethod comprises accessing a dataset from an electronic database, thedataset comprising data collected from one or more patients and spinalsurgical strategy employed for the one or more patients. In someembodiments, the system is configured to divide the dataset into one ormore categories based on spinal surgery domain knowledge. In someembodiments, the system is configured to separate the data, for eachcategory, into a first subcategory and a second subcategory, wherein thefirst subcategory is used for training and the second subcategory is fortesting the predictive model.

In some embodiments, the system is configured to standardize the data inthe first subcategory. In some embodiments, the system is configured toselect a model algorithm to the data in the first subcategory. In someembodiments, the system is configured to input a first set of inputvalues from the first subcategory into the model algorithm to train thepredictive model based on a first set of output values from the firstsubcategory. In some embodiments, the system is configured to input asecond set of input values from the second subcategory into the trainedpredictive model and compare results generated by the trained predictivemodel with a second set of output values from the second subcategory. Insome embodiments, the system is configured to store in a data repositorythe trained predictive model for implementation or future use. In someembodiments, the post-operative parameters comprise one or more ofthoracic kyphosis or pelvic tilt. In some embodiments, the systemcomprises a computer processor and electronic memory.

In some embodiments, the data collected from one or more patientscomprises one or more of an x-ray or clinical information. In someembodiments, the surgical strategy employed for the one or more patientscomprises data relating to one or more of upper instrumented vertebra orlower instrumented vertebra. In some embodiments, the spinal surgerydomain knowledge comprises one or more of adult cases or pediatriccases. In some embodiments, the model algorithm comprises one or more ofa neural network, support vector regression, linear model, and/or thelike. In some embodiments, the model algorithm is selected using across-validation strategy.

In some embodiments, the system is configured to perform acomputer-implemented method for generating a predictive model forestimating post-operative thoracic kyphosis and/or pelvic tiltparameters, wherein the computer-implemented method comprises accessinga dataset from an electronic database, the dataset comprising data fromspinal surgeries, wherein the spinal surgeries involve at least an upperinstrumented vertebra and a lower instrumented vertebra. In someembodiments, the system is configured to analyze the dataset to dividethe dataset into a plurality of categories, the plurality of categoriescomprising a first category comprising data from surgeries, wherein theupper instrumented vertebra is positioned between L1 and L5 vertebraeand the lower instrumented vertebra is positioned between S1 and iliac.

In some embodiments, the system is configured to select the firstcategory, and access the data from the surgeries, the data comprisingone or more of patient ages, pelvic incidence pre-operative values,pelvic tilt pre-operative values, lumbar lordosis pre-operative values,thoracic kyphosis pre-operative values, sagittal vertical axispre-operative values, lower instrumented vertebra values, upperinstrumented vertebra values, and/or lumbar lordosis post-operativetarget values for each of the surgeries in the first category. In someembodiments, the system is configured to standardize the data in thefirst category.

In some embodiments, the system is configured to separate the data intoa first subcategory and a second subcategory, wherein the firstsubcategory is used for training and the second subcategory is fortesting the predictive model for determining the post-operative thoracickyphosis and pelvic tilt parameters. In some embodiments, the system isconfigured to input pre-operative data values in the first subcategoryinto a plurality of statistical models to train the statistical modelsbased on the post-operative data values. In some embodiments, the systemis configured to input pre-operative data values in the secondsubcategory into the plurality of trained statistical models and compareone or more output values from the plurality of trained statisticalmodels with post-operative data values in the second subcategory.

In some embodiments, the system is configured to select a first trainedstatistical model from the plurality of trained statistical models,wherein the first trained statistical model generated one or more outputvalues nearest to the post-operative data values based on the comparing.In some embodiments, the system is configured to store in electronicmemory the first trained statistical model. In some embodiments, thesystem comprises a computer processor and electronic memory.

In some embodiments, the system is configured to perform acomputer-implemented method for generating a surgical plan based on apredictive model for estimating post-operative parameters, thecomputer-implemented method comprising accessing one or more medicalimages of a portion of a spine of a patient. In some embodiments, thesystem is further configured to analyze the one or more medical imagesto determine one or more pre-operative variables relating to the spineof the patient, wherein the one or more pre-operative variables compriseat least one of UIL, LIL, age of the patient, pelvic incidencepre-operative values, pelvic tilt pre-operative values, lumbar lordosispre-operative values, thoracic kyphosis pre-operative values, and/orsagittal vertical axis pre-operative values. In some embodiments, thesystem is configured to generate a prediction of one or morepost-operative variables based at least in part on applying a predictivemodel, wherein the predictive model is generated by one or more of thefollowing processes.

In some embodiments, the predictive model is configured to access adataset from an electronic database, the dataset comprising datacollected from one or more previous patients and spinal surgicalstrategy employed for the one or more previous patients. In someembodiments, the predictive model is configured to divide the datasetinto one or more categories based on spinal surgery domain knowledge. Insome embodiments, the predictive model is configured to standardize thedata in the first subcategory.

In some embodiments, the predictive model is configured to select amodel algorithm to the data in the first subcategory. In someembodiments, the predictive model is configured to input a first set ofinput values from the first subcategory into the model algorithm totrain the predictive model based on a first set of output values fromthe first subcategory. In some embodiments, the predictive model isconfigured to input a second set of input values from the secondsubcategory into the trained predictive model and compare resultsgenerated by the trained predictive model with a second set of outputvalues from the second subcategory.

In some embodiments, the post-operative parameters of the predictivemodel comprise one or more of thoracic kyphosis and/or pelvic tilt. Insome embodiments, the system is configured to generate a surgical planbased at least in part on the predicted one or more post-operativevariables generated by the predictive model. In some embodiments, thesurgical plan comprises at least one of a number of cages forimplantation, location of implantation of cages, length of a spinal rodfor implantation, or curvature of the spinal rod. In some embodiments,the system comprises a computer processor and electronic memory.

In some embodiments, the system is configured to perform acomputer-implemented method for generating a surgical plan based on apredictive model for estimating post-operative thoracic kyphosis andpelvic tilt parameters, the computer-implemented method comprisingaccessing one or more medical images of a portion of a spine of apatient. In some embodiments, the system is further configured toanalyze the one or more medical images to determine one or morepre-operative variables relating to the spine of the patient, whereinthe one or more pre-operative variables comprise at least one of UIL,LIL, age of the patient, pelvic incidence pre-operative values, pelvictilt pre-operative values, lumbar lordosis pre-operative values,thoracic kyphosis pre-operative values, and/or sagittal vertical axispre-operative values. In some embodiments, the system is configured togenerate a prediction of one or more post-operative variables based atleast in part on applying a predictive model, wherein the predictivemodel is generated by one or more of the following processes.

In some embodiments, the predictive model is configured to access adataset from an electronic database, the dataset comprising data fromspinal surgeries, wherein the spinal surgeries involve at least an upperinstrumented vertebra and a lower instrumented vertebra. In someembodiments, the predictive model is configured to analyze the datasetto divide the dataset into a plurality of categories, the plurality ofcategories comprising a first category comprising data from surgeries,wherein the upper instrumented vertebra is positioned between L1 and L5vertebrae and the lower instrumented vertebra is positioned between S1and iliac.

In some embodiments, the predictive model is configured to select thefirst category, and access the data from the surgeries, the datacomprising one or more of patient ages, pelvic incidence pre-operativevalues, pelvic tilt pre-operative values, lumbar lordosis pre-operativevalues, thoracic kyphosis pre-operative values, sagittal vertical axispre-operative values, lower instrumented vertebra values, upperinstrumented vertebra values, and/or lumbar lordosis post-operativetarget values for each of the surgeries in the first category. In someembodiments, the predictive model is configured to standardize the datain the first category.

In some embodiments, the predictive model is configured to separate thedata into a first subcategory and a second subcategory, wherein thefirst subcategory is used for training and the second subcategory is fortesting the predictive model for determining the post-operative thoracickyphosis and pelvic tilt parameters. In some embodiments, the predictivemodel is configured to input pre-operative data values in the firstsubcategory into a plurality of statistical models to train thestatistical models based on the post-operative data values. In someembodiments, the predictive model is configured to input pre-operativedata values in the second subcategory into the plurality of trainedstatistical models and compare one or more output values from theplurality of trained statistical models with post-operative data valuesin the second subcategory.

In some embodiments, the predictive model is configured to select afirst trained statistical model from the plurality of trainedstatistical models, wherein the first trained statistical modelgenerated one or more output values nearest to the post-operative datavalues based on the comparing. In some embodiments, the predicted one ormore post-operative variables comprises at least one of lumbar lordosispost-operative target values, thoracic kyphosis post-operative values,or sagittal vertical axis post-operative values. In some embodiments,the system is configured to generate a surgical plan based at least inpart on the predicted one or more post-operative variables. In someembodiments, the surgical plan comprises at least one of a number ofcages for implantation, location of implantation of cages, length of aspinal rod for implantation, and/or curvature of the spinal rod. In someembodiments, the system comprises a computer processor and electronicmemory.

Sample Data Elements/Parameters for Predictive Modeling

In some embodiments, in order to perform one or more processes ortechniques relating to predictive modeling, the system can be configuredto receive, access, and/or obtain one or more of the following dataelements or parameters that can be collected from one or more patients.

In particular, in some embodiments, the system can be configured toreceive, access, and/or obtain one or more demographic characteristics,such as for example, age at surgery, gender, height, weight, activitylevel, date of narcotics, disability, education, home care requirements,insurance coverage, job, race, date of return to work/school/sport,socioeconomic status, and/or the like.

In some embodiments, the system can be configured to receive, access,and/or obtain one or more patient-reported outcomes, such as forexample, Oswestry Disability Index (ODI), Neck Disability Index (NDI),Scoliosis Research Society (SRS-22), Nurick, and/or the like.

In certain embodiments, the system can be configured to receive, access,and/or obtain one or more radiographic parameters, such as for example,preoperative and/or postoperative data such as T4-T12 Thoracic Kyphosis(TK), L1-S1 Lumbar Lordosis (LL), Sagittal Vertical Axis (SVA), PelvicTilt (PT), Pelvic Incidence (PI), Lordosis, and/or the like.

In some embodiments, the system can be configured to receive, access,and/or obtain one or more other radiographic parameters as well, such asCentral Sacral Vertical Line (CSVL), C2T1 Pelvic Angle (CTPA,°), C2C7SVA (mm) (Sagittal Vertical Axis), Cervical Lordosis, LenkeClassification, Proximal Junctional Kyphosis (PJK), Rod Tracing, SS, T1Slope (T1S,°) T1 Tilt Angle and Direction, T10-L2 angle, T12-S1 LumbarLordosis (LL), T1-T12, T2-T12, T2-T5, T5-T12 Thoracic Kyphosis, Th Apex,Th Bending films parameters, Th Curves/Cobb angles, Th Curve Levels,(Th/L Lumbar Apex, Th/L Lumbar Curve, Th/L Lumbar Curve Direction ofcurve, Th/L Lumbar Curve Levels), T1 Pelvic Angle (TPA), AnatomicalKyphosis, Anatomical Lordosis, Cobb Angles, Coordinates of all vertebracorners in the sagittal and/or coronal planes and the femoral heads, anyother pre-operative and/or post-operative data like, Computerizedtomography Performed, Tri-Radiate Cartilage, External Auditory Meadus,Pelvic Obliquity, Acetabular Index, and/or the like.

In some embodiments, the systems disclosed herein can be configured togenerate spinal surgical strategies comprising one or more surgical dataparameters, such as Instrumentation Material, Instrumentation Size,Instrumentation Type, Minimal Invasive Surgery (MIS) options, Number ofinstrumented Levels, Osteotomies Performed, Rod Bending shapes and/orAngles, Rod Cutting Parameters, Uppermost Instrumented Parameters, UpperInstrumented Vertebrae (UIV), Lower Instrumented Vertebrae (LIV),Surgeon, surgical techniques (in some embodiments, using one or moremachine learning algorithms to analyze surgeon's surgical techniques tobe able to simulate the surgery and the rod that will match surgeon'sexpectations), radiography as an image, scanner, MRI (image or set ofimages), and/or the like.

In some embodiments, a first set of input values for preoperative and/orpostoperative data can include one or more of the following: T4-T12 TK,L1-S1 LL, SVA, Lowermost Instrumented Vertebrae (LIV), UppermostInstrumented Vertebrae (UIV), Pelvic Tilt, Age at the time of surgery,and/or Pelvic Incidence (PI).

In some embodiments, a first set of output values for preoperativeand/or postoperative data can include the following: T4-T12 TK, L1-S1LL, and Pelvic Tilt.

Additional Features of Predictive Modeling

As discussed herein, various embodiments described herein relate tosystems, methods, and devices for developing spinal implants,treatments, operations, and/or procedures. In some embodiments, thesystems, devices, and methods described herein can be configured toutilize machine learning, predictive modeling, and/or artificialintelligence based on previous surgical outcomes and/or one or moreparameters of previously implanted spinal rods or other implants topredict, design, develop, and/or plan patient-specific spinal rodsand/or other implants prior to surgery. Further, in some embodiments,the systems, devices, and methods described herein can be configured toutilize patient-specific and/or surgeon-specific parameters in itsanalysis to develop a surgical plan prior to spinal surgery. In someembodiments, the generated surgical plan can be surgeon-dependent.

In some embodiments, the systems, devices, and methods described hereincan be configured to design a spinal rod and/or other implant to matchor substantially match a surgical plan desired by the surgeon in theinstrumentation.

In some embodiments, the systems, methods, and devices described hereincan be configured to build a predictive model taking into account thepatient and/or the surgeon. In some embodiments, the systems, methods,and devices described herein can be configured to generate or develop orutilize a patient-specific and/or surgeon-specific predictive model. Insome embodiments, the systems, devices, and methods described herein canbe able to anticipate what the position of the rod and/or the shape ofthe spine and/or the position of the vertebra for each vertebra will bein the instrumentation. In some embodiments, based on a predictive modelutilized, generated, and/or developed, the systems, methods, and devicesdescribed herein can be configured to design, produce, and/or cause toproduce a physical spinal rod that could reach the plan.

In some embodiments, the systems, methods, and devices described hereincan be configured to utilize one or more inputs and/or outputs fortraining or developing a predictive model, artificial intelligencemodel, and/or machine learning model.

In some embodiments, the one or more inputs can comprise one or morepast spinal surgery cases performed by a surgeon and one or moreparameters thereof, such as the rod designed, the position of thevertebrae and/or the position of some specific endplates (which can bepreoperative, planned, and/or post-operative), the spinopelvicparameters (which can be preoperative, planned, and/or post-operative),age, weight, and/or height of the patient, and/or material and/ordiameter of the rod. In some embodiments, the spinopelvic parameters cancomprise lumbar lordosis (LL), pelvic tilt (PT), pelvic incidence (PI),T1 pelvic angle (TPA), sagittal vertical axis (SVA), thoracic kyphosis(TK), and/or any other parameter, including those described herein.

In some embodiments, the one or more outputs can comprise the shape ofthe rod, one or more specifications or parameters thereof, and/or itsposition to match the plan, the shape of the spine depending of theshape of the rod, the position of the rod (such as, for example,distance with the spine and/or angles with some specific endplatesand/or other lines), and/or guidelines to anticipate the position of therod.

In some embodiments, in order to build the predictive model, thesystems, devices, and methods can be configured to utilize a generativeadversarial network (GAN). In some embodiments, in order to build ordevelop the predictive model, the systems, devices, and methods can beconfigured to utilize one or more GAN-type artificial intelligence (AI)algorithms and/or predictive modeling algorithms. For example, in someembodiments, one or more GAN algorithms can be used to predict theposition of the rod for a specific surgeon. In some embodiments, theinput to the predictive model can comprise the shape of the manufacturedrod and/or the preoperative spine.

In some embodiments, the systems, devices, and methods can be configuredto utilize data augmentation, for example in addition to GAN-type AIalgorithms, predictive modeling algorithms, and/or others. Inparticular, in some embodiments, the systems, devices, and methods canbe configured to use data augmentation to make false columns using aGaussian process. In some embodiments, the data augmentation is used formachine learning training or training the predictive model, but not fortesting.

In some embodiments, one or more algorithms developed or built by thesystems, methods, and devices can be configured to predict the positionof the post-operative rod. In some embodiments, the model built ordeveloped by the systems, methods, and devices can be configured topredict one or more parameters and/or specifications of a spinal rodthat is predicted to result in a desired or actual post-operative spinebased on the preoperative spine.

In some embodiments, one or more algorithms, such a GAN algorithm, canbe used to analyze one or more medical images of a patient for othertasks, such as automatic detection of the vertebra.

In some embodiments, the systems, devices, and methods described hereincan be configured to convert a prior rod used by a surgeon into amathematical object. For example, in some embodiments, if the rod were asegment, the system can be configured to convert the rod into amathematical object as follows: [A;B], with A coordinates of the firstpoint of the segment and B the last point of the segment. As such, insome embodiments, the system can identify and/or analyze a rod as a setof numbers that can be used by one or more algorithms and/or predictivemodels of the system without losing any information. In someembodiments, the systems, methods, and devices described herein can beconfigured to convert the rods into one or more Bezier curves,b-splines, sequences of segments and/or arc, and/or the like. In someembodiments, the one or more parameters used by the one or morealgorithms and/or predictive models as inputs and/or outputs can dependon the mathematical object chosen to convert the rod into a model. Insome embodiments, the systems, methods, and devices described herein canbe configured to consider the whole shape of the rod.

In some embodiments, the systems, methods, and devices described hereincan be configured to analyze, identify, and/or determine the position ofthe vertebrae and/or specific endplates (preoperative, planned, and/orpost-operative), such as by using coordinates. In some embodiments, thesystems, methods, and devices can be configured to use one or morecoordinates of the vertebrae as input and/or output parameter(s) for amodel and/or algorithm.

In some embodiments, the systems, methods, and devices can not onlyidentify similar previous surgical cases from a database but also buildone or more predictive models and/or algorithms based on the same.

As discussed herein, in some embodiments, predictive modeling,artificial intelligence, and/or machine learning can play an importantrole in preoperative surgical planning for patient-specific spinalsurgery, treatments, implant manufacturing or selection, and/or thelike.

FIG. 4 is a flowchart illustrating an example embodiment(s) ofpredictive modeling and its role in developing patient-specificimplants, treatments, operations, and/or procedures. As illustrated inFIG. 4 , in some embodiments, input for a predictive model of the systemcan comprise one or more patient inputs and/or surgeon inputs. Forexample, in some embodiments, a patient input can include one or morepatient baseline conditions. In some embodiments, a surgeon input caninclude one or more surgical objectives of the surgeon. In someembodiments, the system and/or predictive model thereof can beconfigured to facilitate collection of one or more inputs that can becritical, such as the patient's condition and/or the surgeon'sobjectives.

In some embodiments, the system and/or a predictive model thereof can beconfigured to analyze the input to determine and/or identify, forexample, one or more spinopelvic parameters and/or surgeon preferences.In some embodiments, one or more spinopelvic parameters and/or surgeonpreferences can be additional inputs for the system and/or predictivemodel thereof. In some embodiments, inputs for the system and/orpredictive model thereof can include one or more published literatureand/or analytics from previous cases from the system database.

In some embodiments, the system and/or predictive model thereof can beconfigured to take into account one or more of the aforementioned inputsto generate one or more surgical simulations. In some embodiments, thesystem and/or predictive model thereof can generate one or multiplesurgical simulations based on different assumptions and/or inputs. Insome embodiments, the system can transmit or otherwise provide the oneor more surgical simulations generated by the system or predictive modelthereof to a medical personnel or surgeon, who can review, approveand/or provide other feedback to the system.

FIG. 5 is a flowchart illustrating an example embodiment(s) ofpredictive modeling and its role in developing patient-specificimplants, treatments, operations, and/or procedures. As illustrated inFIG. 5 , in some embodiments, the system and/or predictive model thereofcan be configured to determine and/or predict a surgical outcome of whatis likely to occur above and/or below the instrumentation or spinal rodimplant. For example, in some embodiments, the system or predictivemodel thereof can be configured to analyze one or more preoperativeimages of a spine of a patient, such as for example a sagittal and/orfrontal x-ray image. In some embodiments, the system or predictive modelthereof can be configured to analyze the one or more inputted images andplan what parameters to predict, which can be time-dependent parametersor variables. For example, such parameters for prediction can includepredicted thoracic kyphosis (TK) at 1 year from spinal surgery and/orpelvic tilt (PT) at 1 year from spinal surgery.

In some embodiments, the system and/or predictive model thereof cangenerate one or more predictions, such as of surgical outcome and/orothers, based on one or more inputs, such as preoperative lumbarlordosis (LL), preoperative thoracic kyphosis (TK), pelvic incidence(PI), pelvic tilt (PT), sagittal vertical axis (SVA), patient age,gender, height, weight, and/or the like. In some embodiments, thesystem, based on the predicted outcome or result, can be configured togenerate a surgical plan. In some embodiments, after surgery, the systemcan be configured to analyze one or more postoperative images of a spineof a patient, such as for example a sagittal and/or frontal x-ray image,which can be used to compare to the preoperative prediction to furthertrain the algorithm.

In some embodiments, the system can be configured to analyze one or morepreoperative and/or postoperative medical images of a spine, such as forexample x-ray images, CT images, MR images, and/or the like, and/ormeasurements therefrom. In some embodiments, the system or predictivemodel thereof can be configured to simulate one or more surgicalgestures and/or implants, such as spinal rods and/or cages.

In some embodiments, the system or predictive model thereof can beconfigured to query one or more of patient demographics, measurements,and/or instrumentation data, such as upper instrumented vertebrae (UIV)and/or lower instrumented vertebrae (LIV) to determine one or morecompensatory mechanism. In some embodiments, after the predictive modelhas updated the surgical plan, the plan can then be considered ready forsubmission to the surgeon or medical personnel. In some embodiments, thesystem provides, facilitates, and/or generates multiple planningoptions, data-driven decision support, and/or planning approval.

FIG. 6 is a flowchart illustrating an example embodiment(s) and/oroverview of machine learning in developing patient-specific implants,treatments, operations, and/or procedures. As illustrated in FIG. 6 , insome embodiments, the system is configured to utilize machine learningand/or a training algorithm(s) to generate the predictive model. Inparticular, in some embodiments, the system is configured to utilize asystem database comprising a plurality of previous spinal surgery cases,results thereof, and/or one or more spinal parameters derived therefrom.In some embodiments, the system can utilize such data in a trainingphase and/or testing phase of the machine learning process. In someembodiments, after training and/or testing, the predictive model can beintegrated in the system.

In particular, in some embodiments, the data from the system database orany other database can be split into a training data set and a testingdata set. Splitting the data into a training set and a testing set canbe advantageous, in some embodiments, because testing an algorithm onthe same data it was trained on may not provide accurate testingresults. In some embodiments, machine learning can work with the dataand process it to discover one or more patterns with the parameters tobe used to analyze new data.

In some embodiments, during the training phase, a data scientist orother user can apply one or more machine learning methods to thetraining data. In some embodiments, the system can re-train and/orre-analyze the data to minimize model error. In some embodiments, oncethe model parameters are fixed, the trained predictive model can then betested.

As discussed herein, in some embodiments, the trained predictive modelis tested or assessed with testing data, which can be separate from thetraining data. In some embodiments, after testing is finalized, thepredictive model can be integrated into system tools and then be used topredict one or more output values of the spine of a patient post-surgerybased on one or more preoperative values.

Additional Features of Predictive Modeling

In some embodiments, systems, devices, and methods discussed herein canbe configured to generate and/or utilize one or more predictive modelsto predict the postoperative shape or curvature of a spine of a patient.In some embodiments, this can be advantageous in several aspects. Forexample, in some embodiments, the systems, devices, and methodsdescribed herein can provide information of the length of a spinal rodto be implanted, help anticipate one or more compensatory mechanisms,and/or inform the patient and/or surgeon on one or more results that canbe expected from spinal surgery. As such, in some embodiments, thesystems, devices, and methods described herein can be configured togenerate, build, and/or apply an algorithm that is configured to predictone or more characteristics and/or parameters of a postoperative spinebased on one or more characteristics and/or parameters of a preoperativespine.

In some embodiments, one or more input data and/or collected data for apredictive model can comprise one or more characteristics and/orparameters from a preoperative spine of a subject. In some embodiments,the inputted spine data can comprise coupled data, (Coronalcurve,Sagittalcurve). In some embodiments, each curve or spline can compriseseveral mathematical curves linking one or more identified anatomicallandmarks from one or more medical images measurements. In someembodiments, the one or more identified anatomical landmarks can beidentified automatically, semi-automatically, and/or manually, such asfor example using one or more techniques described herein.

In some embodiments, the system can be configured to apply one or moredata transformation techniques to one or more collected data. Forexample, in some embodiments, the system can be configured to apply ananalog to discrete conversion. In some embodiments, the system can beconfigured to work only with mathematical curves. In some embodiments,the system can be configured to make one or more splines discrete. Inparticular, in some embodiments, for each coronal and/or sagittalspline, the system can be configured to obtain N number of pointsuniformly distributed along the vertical axis between the inferior andsuperior points of the spline.

In some embodiments, the system can be configured to transform thecollected or inputted data from two dimensions to three dimensions, forexample to prepare the data for machine learning. In particular, in someembodiments, the system can be configured to create or generate a singlethree-dimensional object that represents the spine of a subject based ontwo or more two-dimensional representations of the spine. In someembodiments, in order to do so, the system can be configured to assumethat the coronal and sagittal x-ray images are perpendicular, that bothcalibration ratios of the two x-ray images are perfectly accurate,and/or that both x-ray images were taken simultaneously in time.

In some embodiments, the system can be configured to assume that thex-axis extends from the back of the patient to the chest, that they-axis extends from the right side of the patient to the left side,and/or that the z-axis is vertical and ascending. In some embodiments,such assumptions can define a direct landmark. In some embodiments, thesystem can be configured to utilize a polar representation of thethree-dimensional spine instead of a Cartesian representation. In someembodiments, the system can be configured to utilize a Cartesianrepresentation of the spine.

In some embodiments, the system can be configured to utilize one or moredata compression techniques and/or algorithms. For example, in someembodiments, the system can be configured to utilize a low-pass filterfor data compression, which can be advantageous for machine learningpurposes, while preserving most of the relevant information.

In some embodiments, based at least in part on the transformed input orcollected data, the system can be configured to apply one or moremachine learning techniques and/or algorithms. In some embodiments, thesystem can be configured to utilize one or more machine learning,artificial intelligence, and/or predictive modeling algorithms topredict one or more postoperative parameters, such as a spinal curvaturefor example, knowing only one or more preoperative and/or potentiallyplanned parameters. In particular, in some embodiments, the system canbe configured to utilize one or more linear models and/or neuralnetworks.

In some embodiments, the system can be configured to utilize one or moremachine learning, artificial intelligence, and/or predictive modelingalgorithms to simulate the outcome of spinal surgery. In particular, insome embodiments, depending on the data transformation done throughinput data preparation, if any, the system can be configured to convertthe direct output of the model through a reversed process of the datatransformation or data compression algorithm employed to obtainpredicted one or more postoperative parameters. As such, in someembodiments, the system can be configured to utilize one or more machinelearning, artificial intelligence, and/or predictive modeling algorithmsto predict one or more postoperative spinal parameters, such as a spinalcurvature.

FIG. 7 is a flowchart illustrating an example embodiment(s) ofpredictive modeling in developing patient-specific implants, treatments,operations, and/or procedures. As illustrated in FIG. 7 , in someembodiments, the systems, devices, and methods described herein areconfigured to utilize predictive modeling, artificial intelligence,and/or machine learning in predicting the surgical outcome of spinalsurgery based in part on one or more of one or more medical images ofthe patient's spine, parameters of the patient's spine, specificationsof a proposed implant, such as a spinal rod or cage, demographicinformation about the patient, and/or the like.

In some embodiments, as illustrated in FIG. 7 , one or more inputs areinputted into the system and/or predictive model thereof for predictingthe surgical outcome and/or building the predictive model at block 702.In some embodiments, one or more medical images can be inputted into thesystem as shown in block 704. The one or more medical images can be oneor more x-ray images of the spine, such as sagittal and/or frontal x-rayimages, CT images, MR images, and/or the like. In some embodiments, theone or more inputs can include one or more other information, such asnon-imaging data or inputs, as shown in block 706. The one or more otherinformation can include the surgeon's name, one or more preferences ofthe surgeon, one or more demographic information of the patient, such asheight, weight, age, medical condition(s), and/or the like. Further, insome embodiments, the one or more inputs at block 702, whether medicalimages 704 or non-imaging data 706, can comprise one or morepreoperative and/or postoperative data, for example to train and/orbuild the predictive model. As an example, in some embodiments, thepreoperative data can comprise one or more spinal parameters prior tosurgery and the postoperative data can comprise one or more spinalparameters and/or specifications of one or more spinal implants, such asa rod, cage, screw, and/or the like.

In some embodiments, as illustrated in FIG. 7 , the system can beconfigured to determine one or more measurements from the one or moreinputs at block 708. In particular, in some embodiments, the system canbe configured to measure one or more specific points and/or parameterson one or more medical images. For example, in some embodiments, thesystem can be configured to determine and/or identify, automatically orsemi-automatically or manually, one or more points on each endplateand/or vertebrae and/or a position and/or one or more angles of eachendplate and/or vertebrae. As an example, in some embodiments, thesystem can generate a user interface that allows a user to pinpoint oneor more points on the edge of one or more endplates and/or vertebraeand/or one or more positions and/or angles of one or more endplatesand/or vertebrae using a mouse, touch, or other computer input method.As another example, in some embodiments, the system can be configured toautomatically determine the boundary of each endplate, for example usingedge detection techniques, and/or automatically determine one or moreangles of each vertebrae. Some examples of measurements can includepreoperative lumbar lordosis (LL), preoperative thoracic kyphosis (TK),pelvic incidence (PI), pelvic tilt (PT), sagittal vertical axis (SVA),position, boundary, position of femoral head, and/or any otherspinopelvic parameters of one or more endplates and/or vertebrae.

In some embodiments, the system can be configured to take one or moremeasurements of one or more spinopelvic parameters directly from a CTimage, MM image, or other three-dimensional medical image of the spineof a patient. In some embodiments, the system can be configured toanalyze one or more x-ray images of the spine of a patient to firstdetermine or extract the position of one or more vertebrae and thendetermine one or more spinopelvic parameters based on the extractedposition of one or more vertebrae. For x-ray image based analysis, insome embodiments, it can be advantageous to extract the position of oneor more vertebrae first before taking measurements of one or morespinopelvic parameters, because some information can be lost ifmeasurements are taken directly from x-ray images due to transformationof the data in the measurement step. This can be different fromembodiments that use CT or Mill images, because for CT or Mill images,no information can be lost by taking direct measurements of spinopelvicparameters. As such, in some embodiments that utilize one or more x-rayimages, the system can be configured to first extract the position ofone or more or every vertebrae on the image to obtain a largerfundamental set of data that can be used later to further extract one ormore angles and/or other spinopelvic parameters. That way, in someembodiments, once the system has determined the position of one or moreor each vertebrae, the system can then be configured to furtherdetermine any angle and/or any spinopelvic parameter as necessarywithout losing information.

In some embodiments, as illustrated in FIG. 7 , the system is configuredto clean some of the collected data or measurements at block 710. Insome cases, one or more measurements taken from the one or more medicalimages may not be reliable or clean. If unreliable or unclean data isinputted into a predictive modeling or machine learning algorithm, theoutput or prediction of the post-surgical outcome may not be reliable aswell. As such, in some embodiments, the system can be configured todiscard or remove one or more measurements if the quality does not meeta predetermined or preset threshold.

In some embodiments, as illustrated in FIG. 7 , the system can beconfigured to transform the data or measurements or perform a datatransformation at block 712. In some embodiments, there can be atrade-off between the number of parameters and the complexity of thepredictive model. In other words, in some embodiments, it can beadvantageous to limit the number of parameters inputted into thepredictive model and/or used to build the predictive model in order tomaintain the complexity of the predictive model at a manageable level.Also, if a large number of parameters are used, a large dataset may alsobe required to train the predictive model to an acceptable accuracylevel. At the same time, however, in some embodiments, it can beadvantageous to have a large dataset or large number of cases intraining the predictive model. As such, for training purposes, in someembodiments, the system can be configured to train a predictive modelbased on a large dataset or large number of cases but with a limited orrestricted number of parameters. In some embodiments, the system can beconfigured not to directly use all of the measurements or the inputdata, such as measurements taken from an x-ray image(s), MRI image, orCT image for example, but rather use a subset of parameters thereof. Insome embodiments, both preoperative and/or postoperative parameters ordata can be transformed using one or more data transformation techniquesas discussed herein. In some embodiments, the preoperative andpostoperative parameters can be transformed using the same or differenttechnique. Further, in some embodiments, the preoperative andpostoperative parameters being transformed can the same or different.For example, the preoperative and postoperative parameters can be of aspine, compensation mechanism, spinal rod, whether before or afterimplantation, cage, screw, and/or any other spinal implant or parameter.

In some embodiments, data transformation can comprise mathematicalmodeling, such as manipulating one or more mathematical objects. In someembodiments, data transformation can comprise data compression, forexample to reduce the number of input parameters as discussed here. Insome embodiments, data transformation can comprise data compression toobtain the minimum pertinent parameters, which can refer to a subset ofparameters with the most information. In other words, in someembodiments, the system can be configured to only use those parameterswith the most information as possible.

In some embodiments, the system can be configured to utilize one or moredata compression techniques, such as Fourier transformation and/or apolynomial function. In particular, in some embodiments, from theinputs, the system can obtain a measurement of the shape of a spine,which can comprise a plurality of short straight segments. In someembodiments, the system can determine and save the angle between eachshort straight segment and a vertical line, which can be modeled througha Fourier transformation to have less parameters than before. In someembodiments, after a Fourier transformation, only a portion of theparameters with more information or more accurate information can bekept for use in training and/or applying the predictive model.

In some embodiments, after applying a Fourier transformation to thedata, the system can be configured to keep only low frequency data orparameters and discard high frequency data or parameters for purposes oftraining and/or applying the predictive model. As will be discussedherein, in some embodiments, the data transformation can later bereversed to obtain data for each parameter or segment in the real worldor spatial domain, as opposed to the Fourier transformation world orfrequency domain. In other words, in some embodiments, the system can beconfigured to apply a Fourier transformation to the measured dataset andthen filter out high frequency data such that only low frequency dataremains, because high frequency data can comprise a large number ofnoise or unclear data, thereby reducing the quality of data to use tobuild a mathematical model of the spine. Further, in some embodiments,the system can be configured to reverse or inverse the Fouriertransformation afterwards to convert the data and/or angles back to thereal world or spatial domain. In other words, in some embodiments, thesystem can be configured to utilize Fourier transformation and thenapply a low frequency filter with a predetermined frequency threshold asa technique for filtering out noise or high frequency data or parametersin the frequency domain to improve the quality of the predictive modeland/or output from the predictive model. That way, in some embodiments,the system can generate a model of a preoperative and/or postoperativespine based only on low frequency data in the Fourier world, which canbe converted back to the real world or spatial domain.

In addition, by utilizing a mathematical model and/or datatransformation rather than working directly from the input, in someembodiments, a predictive model built on one modality of medicalimaging, such as for example x-ray, can be applied to medical imaginginput from another modality, such as for example CT or MRI, as long asit is converted to 3D or 2D as necessary. For example, in someembodiments, by building or training a predictive model by utilizing amathematical model and/or data transformation to x-ray image(s), suchpredictive model can also be applied to CT and/or MRI images afterconverting to three-dimensional space.

In some embodiments, as illustrated in FIG. 7 , the system is configuredto augment the data at block 714, which can be optional in certainembodiments. In order to augment the data, in some embodiments, thesystem can be configured to create artificial data based on the actualor real dataset, for example to improve the training of the predictivemodel and/or obtain a more complex model. In some embodiments, theaugmented and/or artificial data generated by the system can be based onpreoperative data and/or curvature of a spine and/or postoperative dataand/or curvature of a spine and/or rod or other spinal implant, forexample for purposes of training the predictive model.

As discussed herein, in some embodiments, it can be advantageous totrain a predictive model using a large dataset with a large number ofcases. In addition, in some embodiments, having a larger set of data canallow the system to utilize a more complex algorithm for predictivemodeling. For example, in some embodiments, by using a larger set ofdata, whether it is by augmented the data or by initially starting witha large set of data, the system can utilize a Convolutional NeuralNetwork (CNN) algorithm to train the predictive model. In addition, insome embodiments, as the system can obtain a larger dataset through dataaugmentation, it can be possible to utilize more parameters for trainingthe predictive model. Further, in some embodiments, as the system canobtain a larger dataset through data augmentation, it can be possible toidentify even better parameters for training the predictive model.Furthermore, in some embodiments, as the system can obtain a largerdataset through data augmentation, it can be possible to obtain a largerset of clean data for training the predictive model

As such, in some embodiments, the system augments the dataset fortraining the model by creating artificial data, but not for testing thepredictive model. In other words, in some embodiments, the system can beconfigured to use augmented data to increase the learning dataset fortraining the predictive model, but not for testing the predictive model.In some embodiments, augmented or artificial data is never used fortesting a predictive model.

In some embodiments, the system can be configured to augment the datasetby applying a Gaussian process or another statistical and/ormathematical technique to the actual data. As an example, in someembodiments, the system can be configured to consider a spine of apatient to comprise one or more vectors, in which case the system can beconfigured to utilize the sampling function of a Gaussian process oneach vector to generate one or more artificial inputs or spinalcurvatures. As another example, in some embodiments, augmented data cancomprise one or more parameters that are different or off from theactual or real data by some statistically and/or predeterminedacceptable threshold, for example within about 1%, about 2%, about 3%,about 4%, about 5%, about 10%, about 0.5 standard deviation, about 1standard deviation, about 1.5 standard deviation, about 2 standarddeviations, and/or within a range defined by two of the aforementionedvalues. As such, in some embodiments, the system can be configured tobuild artificial data points or cases from the actual dataset based onstatistics, which can then be used to train the model.

In some embodiments, the system can be configured to generate augmentedor artificial data by a non-statistical process or technique. Forexample, in some embodiments, the system can be configured to generateaugmented or artificial data by flipping or rotating in 180 degrees ormirroring a particular curvature of a spine, whether from the frontalview or sagittal view. In particular, in some embodiments, the systemcan be configured to flip the left-right orientation of a curve of aspine as depicted on a medical image, whether from a sagittal or frontalview, which can be the basis for generating augmented or artificialdata. In addition, in some embodiments, the system can be configured torotate the orientation of a curve of a spine as depicted on a medicalimage, whether from a sagittal or frontal view, along a vertical axis,which can be the basis for generating augmented or artificial data. Insome embodiments, the degree of rotation of the spine along the verticalaxis, either left or right, for generating the augmented or artificialdata can be about 10 degrees, about 20 degrees, about 30 degrees, about40 degrees, about 50 degrees, about 60 degrees, about 70 degrees, about80 degrees, about 90 degrees, about 100 degrees, about 110 degrees,about 120 degrees, about 130 degrees, about 140 degrees, about 150degrees, about 160 degrees, about 170 degrees, about 180 degrees, and/orwithin a range defined by two of the aforementioned values.

In some embodiments, the system can be configured to generate augmentedand/or artificial data by combining one or more techniques discussedherein, such as for example using a Gaussian process and/or rotation ofa spine from actual data. In some embodiments, data can be augmented bythe system based on either before or after data transformation accordingto one or more data transformation techniques discussed herein, such asa Fourier transformation.

In some embodiments, as illustrated in FIG. 7 , the system is configuredto train a predictive model and/or generate one or more postoperativepredictions, for example using one or more machine learning techniquesor neural networks, at block 716. In some embodiments, the system can beconfigured to utilize one or more of a Generative Adversarial Network(GAN) algorithm, a Convolutional Neural Network (CNN) algorithm, and/ora Recurrent Neural Network (RNN) algorithm, linear regression, SupportVector Machine (SVM) algorithm, Support Vector Machine—Regression (SVR)algorithm, and/or any combination thereof. For example, in someembodiments, the system can be configured to utilize a combination of aCNN algorithm with an SVM algorithm.

In some embodiments, the system can be configured to utilize one or moremachine learning algorithms and/or any combination thereof to train apredictive model and/or generate one or more postoperative predictionson varying numbers of inputs. For example, in some embodiments, thesystem can be configured to take as an input data one or more parametersof one vertebra, two vertebrae, three vertebrae, four vertebrae, fivevertebrae, and/or the like, and/or any combination thereof in trainingthe predictive model and/or generating one or more postoperativepredictions.

In some embodiments, the system can be configured to utilize a GANalgorithm to train a predictive model and/or generate one or morepostoperative predictions. In some embodiments, a GAN algorithm can beused to predict a postoperative spine, a part thereof, and/or one ormore parameters thereof. Further, in some embodiments, a GAN algorithmcan be used to predict a rod shape and/or position post-surgery.

In some embodiments, the system can be configured to utilize a CNNalgorithm to train a predictive model and/or generate one or morepostoperative predictions. In some embodiments, the system can beconfigured to utilize data augmentation to help to be able to use a CNNalgorithm. In some embodiments, the system can be configured to utilizea CNN algorithm to predict compensatory mechanism, rod position, rodshape, output(s) of surgery, proximal junctional kyphosis (PJK) risks,and/or the like.

In some embodiments, the system can be configured to utilize an RNNalgorithm to train a predictive model and/or generate one or morepostoperative predictions. In some embodiments, the system can beconfigured to utilize an RNN algorithm to deal with variable inputsizes. For example, in some embodiments, the system can be configured touse the targeted position of the endplate instrumented as input, inwhich case the size of the input can depend on the number ofinstrumented vertebrae. As such, in such embodiments, an RNN algorithmcan be useful to deal with the size of the input increasing with thenumber of vertebrae.

In some embodiments, as illustrated in FIG. 7 , the system is configuredto output data that has been transformed back at block 718. Inparticular, in some embodiments, as discussed herein, the system can beconfigured to train a predictive model and/or generate one or morepostoperative predictions in the realm of transformed data, such as forexample after applying a Fourier transformation. As such, in order toobtain usable output data in the real world, in some embodiments, thesystem can be configured transform the data in a frequency domain backinto a spatial domain to make one or more postoperative predictions. Forexample, in some embodiments, the system can be configured to apply aninverse Fourier transformation to output data that was outputted by apredictive model that is in a Fourier transform.

In some embodiments, as illustrated in FIG. 7 , the transformed outputdata can then be utilized at block 720. In particular, in someembodiments, the output can comprise one or more of a predicted surgicaloutcome, results of a compensatory mechanism and/or spinal curvature orother spinopelvic parameters, specifications of a proposed spinal rod orother spinal implant, one or more proposed surgical steps, and/or thelike. In some embodiments, the output data can comprise one or more ofone or more spinopelvic parameters, shape of the coronal and/or sagittalspine, position of one or more endplates, shape of a spinal rod to beimplanted, and/or the like and/or any combination thereof.

Intraoperative Tracking

FIG. 8 is a flowchart illustrating an example embodiment(s) of implantproduction, case support, data collection, and/or intraoperativetracking during spinal surgery for developing patient-specific spinalimplants, treatments, operations, and/or procedures.

In some embodiments, a computing system at an implant production and/orselection facility can be configured to access and/or receive a finalsurgical plan or a plurality thereof at block 802, for example via theInternet, wireless communication, and/or a portable electronic storagemedium. In some embodiments, the implant production facility can beconfigured to produce, modify, and/or select one or more parts for thesurgical procedure at block 804. For example, the implant productionfacility can be configured to produce a spinal rod(s), cage(s), and/orscrew(s) based on one or more specifications and/or materials specifiedin the surgical plan(s). Similarly, the implant production facility canbe configured to select and/or modify one or more pre-produced spinalrods, cages, and/or screws based on specifications and/or materialsspecified in the one or more surgical plans.

In some embodiments, a spinal rod, cage, and/or screw can be producedfrom one or more different materials. The particular material to be usedfor a particular patient-specific rod(s), screw(s), and/or cage(s) candepend on data and/or can be selected by a surgeon, other medicalpersonnel, and/or other user. The particular material can also depend onthe particular patient's height, weight, age, bone density, and/or bonestrength, among others. In some embodiments, the system can beconfigured to design, select, and/or produce one or more of thoracolumbar rods, cervico thoracic rods, MIS rods, and/or 3D bent rods. Incertain embodiments, a spinal rod can be made of titanium, cobalt-chromealloy, and/or any other material.

As discussed above, in some embodiments, the system can be configured toproduce, select, and/or modify a rod that is bent in one or moredirections. Generally, it can be difficult, if not impossible, for asurgeon to bend a rod in even one direction, let alone more than onedirection, using tools prior to or during surgery. In contrast, byutilizing a composite of two-dimensional x-ray images and/orthree-dimensional medical images, the system can be configured toproduce, and/or select from pre-existing inventory, a rod that is bentor curved in more than one direction, for example sideways and also in asagittal direction.

Referring back to FIG. 8 , in some embodiments, one or more medicalpersonnel can select one or more implants, such as spinal rod(s),cage(s), and/or screw(s) for implantation at block 806 that wasproduced, modified, and/or selected by the implant production facilitybased on the surgical plan at block 804.

In some embodiments, one or more medical personnel can attach and/oractivate one or more intraoperative tracking sensors and/or modules forintraoperative tracking at block 808. For example, in some embodiments,one or more intraoperative tracking sensors and/or modules can beattached to one or more implants, such as a spinal screw and/or thelike. In some embodiments, the one or more sensors and/or modules can belocated in one or more screws and/or nuts for attaching to a patient'svertebrae and/or tools for attaching the same. One or more sensorsand/or modules that can be used in certain embodiments are discussed inmore detail below. In some embodiments, for spinal surgeries, a sensorand/or module can be placed in and/or attached to every vertebra. Thiscan be advantageous for providing accurate data. However, this may notbe desirable in some situations due to the size of data. For example, alarge amount of unnecessary data can be collected, when the angle of thevertebrae can be one of the most important parameters. As such, in someembodiments, a sensor and/or module may be attached to only a subset ofvertebrae that can provide valuable position and/or angular data of thespine.

In some embodiments, the system can be configured to utilize datacollected from one or more sensors and/or modules inside and/or attachedto one or more screws implanted into the vertebrae instead of and/or inaddition to relying on imaging techniques, for example assuming that animplanted screw will be parallel to an endplate, in order to provideintraoperative tracking. In other words, in some embodiments, angulationof a screw in a sagittal plane can be assumed to be equal orsubstantially equal to the vertebra angulation. In some embodiments, atop portion of a screw can comprise an active or passive sensor. The topportion can be broken off later during surgery, in some embodiments,such that the sensors can be re-used. The one or more screws comprisingone or more sensors can be inserted into every vertebra or a subsetthereof. For example, in some embodiments, sensors and/or modules can beattached to all 20 vertebrae. In some embodiments, sensors and/ormodules can be attached to only a subset thereof, for example two ormore sensors and/or modules attached to the upper lumbar and/or two ormore attached to one or more lower vertebrae. In some embodiments, thesensors and/or modules can then be utilized for providing data relatingto the position and/or angle or orientation of one or more vertebrae insix degrees of freedom (or nine degrees of freedom) in translation androtation in real-time, near real-time, and/or substantially real-time.In some embodiments, the raw data collected by the one or more sensorsand/or modules can be transmitted to a computer system to translate theraw data into tracking the position and/or orientation of one or morevertebrae, for example to assist in determining a spinal curvatureand/or surgical correction.

In some embodiments, based on real-time, near real-time, and/orsubstantially real-time intraoperative tracking or monitoring, thesystem can be configured to track the position and/or orientation orangulation of the vertebrae and/or screw(s). In other words, in someembodiments, correction of the spine during surgery can be monitored inreal-time, near real-time, and/or substantially real-time. Referringagain to FIG. 8 , in some embodiments, tracking data corresponding tothe position and/or angulation of each vertebra can be transmitted tothe main server system and/or a client system at the medical facility atblock 810.

In certain embodiments, after one or more medical personnel inserts one,two, or more screws into the spine of a patient, the main server systemand/or medical facility client system can be configured to track,analyze, and/or store movement of the different vertebrae during thecorrection and other operating procedure data at block 812. In someembodiments, one or more medical personnel can thus visualize orotherwise track the position, orientation, correction and/or angulationof the vertebrae in real-time, near real-time, and/or substantiallyreal-time and determine when desirable conditions, for example matchinga pre-determined surgical plan, have been obtained. Such live-trackingcan provide substantial assistance to the medical personnel. Forexample, without intraoperative tracking, a surgeon may believe that a30 degree correction can be obtained when PSS is performed; however, inreality, a performed PSS may only result in a 10 degree correction. Byproviding intraoperative tracking or monitoring, in such situations insome embodiments, the surgeon can make further corrections as necessarybefore closing up the operation.

In some embodiments, the system can be configured to conduct analysis ofthe tracked data by comparing the same to a pre-determined surgicalplan. To do so, in some embodiments, the system can retrieve data from aplan database 216 and/or operation database 218. Based on suchcomparison and/or analysis, in some embodiments, the system can beconfigured dynamically generate and/or provide guidance to the surgeonduring the operation in real-time and/or near real-time in block 814.For example, based on the tracked data, in some embodiments, the systemcan be configured to instruct or guide the surgeon to change the angleof one or more vertebra based on the tracked data to obtain a curvatureof the spine closer to the pre-determined plan.

In some embodiments, the system can further be configured to provide anaudible and/or visible alert and/or guidance to the surgeon. In someembodiments, the audible and/or visible alert and/or guidance cancomprise instructions to the surgeon to perform the surgery in aparticular way or degree and/or alert the surgeon when the positionand/or angulation of one or more vertebrae is within a predeterminedthreshold. For example, the system can be configured to provide an alertwhen the position and/or angulation of one or more screws and/orvertebrae is within about 1%, about 2%, about 3%, about 4%, about 5%,about 10%, about 15%, about 20%, about 25% of the predetermined planand/or when within a range defined by two of the aforementioned values.In some embodiments, the system can be configured to provide a visualdepiction of the position, location, orientation, and/or angulation ofeach vertebra on a display based on the tracked data to guide thesurgeon during surgery.

In some embodiments, once an acceptable level of angulation of thevertebrae if obtained, the surgeon can insert a spinal rod and/ortighten the screws to the rod and lock all parts for example to completethe positioning of a spinal rod at block 816. In some embodiments, thesurgeon can then remove and/or deactivate the one or more sensors atblock 818.

The system can further be configured to collect and/or utilizepostoperative data in some embodiments, for example to providepredictive modeling and/or other post-operation features or services.Moreover, in some embodiments, the system can be configured to take intoaccount a level of sophistication and/or preferences of a surgeon toprovide surgeon-specific recommendations for future cases. In someembodiments, comparison and/or analysis of preoperative, intraoperative,and/or postoperative data and/or surgeon input can be used to determinea skill level and/or strategic preferences of a surgeon. In someembodiments, the particular skill level of the surgeon and/or strategicpreferences can be used to develop subsequent surgical planning for thatsurgeon. In addition, in some embodiments, data relating to growth ofthe spine and/or other subsequent developments, such as relating tocurvature, can also be obtained from one or more postoperative x-rayimages. In some embodiments, such long-term effects can also be utilizedin preparing subsequent planning.

In some embodiments, as part of predictive modeling and/or machinelearning as discussed herein, the system can be configured to analyzeone or more different plans that were developed for a particular case.For example, in some embodiments, a first generated plan can be based onthe strategy and/or objectives of a surgeon. In some embodiments, asecond generated plan for the same case can be based on data fromscientific literature. In some embodiments, a third generated plan forthe same case can be based on historical data collected by the systemthrough performance of surgical procedures. In some embodiments, as moredata is collected, and as more feedback and input are given and receivedfrom surgeons, and/or as more scientific research is conducted, the oneor more generated plans and/or particular features thereof for a singlecase may converge. In some embodiments, certain parameters that convergemore so than others can be utilized more heavily by the system inplanning stages for subsequent cases. Further, in some embodiments, thesystem can be configured to compare a given case to previous cases inthe planning stage. For example, in some embodiments, the system can beconfigured to parse one or more databases to find one or more spinesthat match a given case and/or certain features thereof to make certainrecommendations and/or predictions for planning.

Intraoperative Tracking Module(s)

Generally speaking, certain intraoperative imaging such as fluoroscopyand/or CT scans can be used for intraoperative assessment of spinalcurvatures and/or correction thereof. However, such processes generallyonly provide instantaneous vision/assessment of spinal curvatures. Assuch, it can be advantageous to allow live or near-live tracking ofspinal curvatures/angulations to provide substantial assistance to thesurgeon, thereby further allowing the surgeon to make furthercorrections to the spine as may be necessary under live control. At thesame time, certain live-tracking devices, such as those that may bebased on optoelectronic passive sensors, may disturb the surgeon'sworkflow as many additional steps may be required compared to usualsurgery.

Accordingly, in some embodiments described herein, systems, devices, andmethods are provided that allow for intraoperative monitoring, forexample during spinal surgery. In particular, in some embodiments, thesystem can be configured to track a surgeon's performance in real-time,near real-time, and/or in substantially real-time and further comparethe same to the preoperative planning, while adding only a minorfootprint on surgery workflow.

In some embodiments, the system can allow a surgeon to manipulate apatient's spine and follow one or more positions and/or one or moreorientations or angles of one or more sensors and/or modules that areattached to one or more vertebrae. In some embodiments, one or moresensors and/or modules attached to one or more vertebrae can beconfigured to provide tracking data relating to one or more positionsand/or orientations of the vertebra the sensor is attached to. As such,in some embodiments, based on such tracking data and/or guidance dataderived therefrom, the surgeon can then manipulate the patient's spineuntil one or more sensor and/or module readings show that thepositioning of the spine is optimal, desirable, and/or matches orsubstantially matches those of a predetermined plan.

In some embodiments, intraoperative imaging processes or techniques,such as fluoroscopy and/or CT scans can be used for intraoperativeimaging. For example, in some embodiments, intraoperative fluoroscopycan be used to assess the position of screws regarding anatomystructures to provide intraoperative tracking. In some embodiments, oneor more sensors and/or modules can be used in conjunction with one ormore infrared cameras and/or electromagnetic detection. In someembodiments, the position(s) and/or orientation(s) of the one or moresensors and/or modules and/or bones can be identified by use of activesensors and/or modules. In certain embodiments, one or more passivesensors and/or modules can be used.

In some embodiments, the system can be configured to identify theposition(s) and/or orientation(s) of one or more pedicle screws, and inturn one or more bones and/or vertebrae to which the one or more pediclescrews are attached thereto, by use of one or more active and/or passivesensors and/or modules. In some embodiments, the system is configured toutilize one or more active sensors and/or modules, without the need forany receivers to interpret the position, orientation, and/or angulationof one or more sensors and/or modules on a common axis system. In otherwords, in some embodiments, the whole intraoperative tracking systemand/or device may be configured to operate using only one or moresensors and one or more computer devices or systems treating the signalof the one or more sensors and displaying one or more measurementsobtained therefrom.

In some embodiments, an intraoperative tracking sensor and/or module, asthe term is used herein, can comprise a power source, such as a battery,a wireless transmitter, and one or more active and/or passive sensorsfor real-time tracking. In some embodiments, the one or more sensors cancomprise one or more accelerometers and/or one or more gyroscopes toprovide one or more inertial measurement units, such as in 6 degrees offreedom (DOF) and/or 9 DOF. In some embodiments, the system can compriseone or more active sensors, which can be configured to be an inertialmeasurement unit in 6 DOF and/or 9 DOF. In some embodiments in which thesystem is configured to utilize one or more passive sensors and/ormodules, visual tracking can be utilized to provide intraoperativetracking in real-time, near real-time, and/or in substantiallyreal-time. In some embodiments in which only active sensors and/ormodules are used, the system can be configured not to rely on visualtracking. Rather, in some embodiments, the system can utilize wirelesstransmission of motion data for intraoperative tracking in real-time,near real-time, and/or in substantially real-time.

In some embodiments, the system can be configured to determine relativeorientation and/or position of two or more sensors and/or modulesattached to a patient's spine to measure and/or calculate spinalcurvature, for example by interpreting independent sensor data. Inparticular, in some embodiments, the system can be configured tointerpret independent sensor data obtained from two or more sensorsand/or modules, using the gravity force vector as a common referenceaxis. In some embodiments, two of the three axes of each central unitcan be assumed or considered to be on a plane parallel or substantiallyparallel with a determinate angle to the sagittal plane of the patientlying on the operating table. In other words, in some embodiments, theposition and/or orientation of two or more sensors and/or modules can beconfigured such that two of the three axes of position data to becollected by each sensor or module are on or assumed to be on a planeparallel or substantially parallel to the sagittal plane of a patientlying on the operating table. As such, in some embodiments, the rightpositioning of the inertial unit can be mechanically obtained through asensor/implant interface.

In some embodiments, one or more sensors and/or modules can be attachedto every vertebra, for example through one or more interfaces providedvia one or more implants/screws and/or directly to bone structures. Insome embodiments, one or more sensors and/or modules can be attached toonly a portion or subset of the vertebrae. As such, in some embodiments,one or more sensors and/or modules may be attached to only a subset ofvertebra that can provide valuable position and/or angular data of thespine.

FIG. 9 is a schematic illustrating an example embodiment(s) ofintraoperative tracking. As illustrated in FIG. 9 , in some embodiments,one or more intraoperative tracking sensors and/or modules 902 may beattached only to certain vertebrae, for example to which a spinal rod904 is implanted. For example, in some embodiments, one or moreintraoperative sensors and/or modules may be attached to S1, L1 and T4vertebrae to assess L1-S1 lordosis and/or T4-T12 kyphosis.

In some embodiments in which one or more intraoperative sensors and/ormodules are directly linked and/or attached to one or more screws, thesystem can be configured to assume that angulation of a screw in asagittal plane is substantially equal to the vertebra (or superiorendplate) angulation. Optionally, in some embodiments, one or moreintraoperative fluoroscopic images can be used to assess the position ofscrews regarding anatomic structures, such as vertebral endplates, inthe sagittal plane, as well as other planes in some embodiments.

In some embodiments, one or more screws and/or other implants thatcomprise and/or to which one or more intraoperative tracking sensorsand/or modules are attached to can be mono-axial, uniplanar, and/orpoly-axial. In some embodiments where one or more mono-axial screws areused, the system can be configured to follow the position and/or angleof every implanted screw, thereby following the position of a vertebraebased on the screw position. A mono-axial screw may comprise only oneintraoperative sensor and/or module, based on the assumption that everymovement of the screw is due to rigid movement of the vertebrae. Incertain embodiments, a mono-axial screw may comprise one or moreintraoperative tracking sensors and/or modules.

In some embodiments, a poly-axial screw can comprise one or moreintraoperative tracking sensors and/or modules and/or two or moreintraoperative tracking sensors and/or modules, for example to be ableto determine if a particular motion or movement is due to rigid movementof the vertebrae itself or at least partially or wholly because ofmotion between the different portions of the screws, such as in andoutside the vertebra, or non-rigid movement. In some embodiments, thesystem can be configured to determine that a particular movement isrigid movement if there is correlation between the two or more sensorand/or module readings.

In some embodiments, a top portion of a screw and/or other implant cancomprise one or more active and/or passive sensors and/or modules. Insome embodiments, the top portion of a screw and/or other implant canalso comprise a power source, such as a battery, and/or wirelesstransmitter, as well as one or active and/or passive sensors and/ormodules. In some embodiments, the top portion can be broken off andremoved later during surgery prior to completion of surgery. In someembodiments, the intraoperative tracking sensor and/or module, or atleast one or more portions thereof, can then be reused, thrown away,and/or repurposed for future use.

In some embodiments, an intraoperative tracking system or device canrequire at least two or more screws to be attached to the vertebrae,wherein each of the two or more screws comprises at least oneintraoperative tracking sensor and/or module. In certain embodiments, anintraoperative tracking system or device can require at least one, two,three, four, five, six, seven, eight, nine, and/or ten screws comprisingand/or attached to one or more sensors and/or modules to be attached tothe vertebrae. In some embodiments, an intraoperative tracking system ordevice can require a certain range of numbers of screws comprising atleast one sensor and/or module, wherein the range is defined by two ofthe aforementioned values.

In some embodiments, once one, two, three, four, and/or more screwscomprising and/or attached to at least one sensor and/or module areattached to the vertebrae, the system can be configured to obtain one ormore sensor and/or module readings of the current position(s),orientation(s), and/or angle(s) of one or more screws and vertebrae.Based on the reading(s) from the one or more sensors and/or guidancegenerated therefrom, in some embodiments, a surgeon can furthermanipulate the patient's spine as desired. For example, in someembodiments, the intraoperative tracking system and/or device can beconfigured to continuously and/or periodically provide updated trackingdata and/or analysis therefrom, such that the surgeon can manipulate thepatient's spine until one or more sensor readings show that one or morepositioning and/or orientation of the spine are optimal and/or matchesor substantially matches a pre-determined plan.

In some embodiments, the system can also be configured to provide tips,guidance, and/or suggestions to the surgeon to manipulate the spine in acertain manner and/or direction, for example to reach and/or moreclosely follow the predetermined plan. In some embodiments, a surgeoncan implant the spinal rod through one, two, three, four, and/or morescrews once an optimal or desired configuration of the spine isobtained. In some embodiments, after rod implantation, the top portionof screw that comprises the one or more sensors can be broken off andremoved.

In some embodiments, the one or more intraoperative tracking sensorsand/or modules are not provided as part of screws or configured to beattached to screws. Rather, in some embodiments, one or moreintraoperative tracking sensors and/or modules can provided as part ofand/or be configured to be attached to one or more surgical tools, whichcan eventually be used to attach screws to the vertebrae. For example,in some embodiments, a screwdriver, nut driver, or other specific orusual surgical tool configured to attach a pedicle screw, anchorage,and/or other implant can comprise and/or be attached to one or moreactive and/or passive sensors and/or modules for intraoperative trackingpurposes. In some embodiments, an intraoperative tracking system canrequire at least one, two, three, four, five, six, seven, eight, nine,and/or ten surgical tools to comprise and/or be attached to one or moresensors and/or modules. In certain embodiments, an intraoperativetracking system or device can require a certain range of numbers oftools to comprise at least one sensor and/or module, wherein the rangeis defined by two of the aforementioned values.

In some embodiments, a surgical tool comprising one or more sensorsand/or modules for intraoperative tracking purposes can comprise abutton or other signaling mechanism that measures and/or stores thecurrent position and/or orientation data of the surgical tool, forexample in 6 DOF and/or 9 DOF. As such, in some embodiments, once ascrew, anchorage, or other implant is put in place, such as attached toa vertebra, using such surgical tool, the surgeon or other medicalpersonnel can activate the sensor in the tool, thereby detecting and/orproviding orientation and/or position data at that time. As such, insome embodiments, the intraoperative tracking system can be configuredto provide data frozen in time rather than providing real-time trackingdata.

Additional Features of Intraoperative Tracking

As discussed herein, various embodiments described herein relate tosystems, methods, and devices for intra-operative tracking during spinalsurgery. In particular, some embodiments described herein comprise anintraoperative tracking device and/or module that can be attached to apedicle screw that has or is configured to be attached to vertebrae of apatient. In some embodiments, the intraoperative tracking device and/ormodule can comprise one or more accelerometers, gyroscopes, and/or othersensors to detect an orientation and/or position of the device and/ormodule. In some embodiments, when the intraoperative tracking deviceand/or module is attached to one or more screws, data relating to theposition and/or orientation of the device and/or module can be detectedand transmitted to a computer system using one or more transmitters,such as a wireless transmitter, that is part of the device and/ormodule. As such, in some embodiments, a surgeon and/or computer systemcan monitor the position and/or orientation of one or more screwsattached to the vertebrae in real-time or in near real-time duringspinal surgery. By utilizing such data, in some embodiments, the systemcan be configured to track progress of the surgery, for example ascompared to a preoperatively determined surgical plan. For example, insome embodiments, the system can be configured to determine how closelya surgeon is performing surgery according to a preoperatively determinedsurgical plan.

In some embodiments, as discussed herein, an intraoperative trackingmodule and/or device can comprise one or more inertial sensors. In someembodiments, the an intraoperative tracking module and/or device can beconfigured to be coupled to, attached to, and/or otherwise associated toa vertebral screw. In some embodiments, an intraoperative trackingmodule and/or device and/or sensor therein can be configured to measurean orientation of a screw to which the module and/or device and/orsensor is attached to. In some embodiments, an intraoperative trackingmodule and/or device can be for single use purposes. In someembodiments, an intraoperative tracking device and/or module can beassembled in a sterilized environment.

In some embodiments, as discussed herein, an intraoperative trackingmodule and/or device and/or sensor thereof can be configured to ensureand/or facilitate application of required or preplanned spinalcorrection during surgery, for example as compared to a predeterminedsurgical plan. In some embodiments, an intraoperative tracking moduleand/or device can comprise one or more wireless transmitters fortransmitting the tracked data to a computer system. For example, in someembodiments, an intraoperative tracking module and/or device can beconfigured to transmit the tracked data via Bluetooth and/or BluetoothLow Energy (BLE) to a computer system.

In some embodiments, a computer system can be configured to receive thetracked data during surgery from one or more intraoperative trackingdevices and/or modules and display the same in some format on a displayor user interface. In some embodiments, the computer system and/or userinterface and/or software operating thereon can provide real-time, nearreal-time, or substantially real-time display to a user or surgeon ofthe intraoperative tracking data. In particular, in some embodiments,the system can be configured to generate and/or provide an visual angledisplay and/or access to one or more patient sagittal parameters orother spinopelvic parameters.

As discussed herein, in some embodiments, a surgeon can attach a spinalrod to one or more screws attached to vertebrae of a patient duringsurgery. As some embodiments herein provide intraoperative trackingcapabilities, in some embodiments, it can be possible to ensure that thespinal rod is correctly attached to the vertebrae of the patientaccording to a preoperatively determined surgical plan. In someembodiments, once the position of the spinal rod is finalized, thetracking device(s) and/or module(s) attached to the screw(s) can bedetached prior to conclusion of the surgery. In some embodiments, anintraoperative tracking device and/or module can be pre-mounted to eachscrew or be provided separately for attachment to a screw during orprior to surgery.

Compatibility of Some Embodiments of Intraoperative Tracking Modules

In some embodiments, an intraoperative tracking device and/or module canbe placed inside a head of a screw, which can either be selectivelyattached to a screw during surgery and/or be pre-mounted to a screwprior to surgery. In some embodiments, the head of the screw and/orsensor can be designed to fit, be compatible with, and/or be part of thesame system of other medical devices and/or surgical tools provided bythe same provider and/or different provider. For example, in someembodiments, the head of the screw and/or sensor can be part of the samesystem as one or more patient-specific spinal rods, patient-specificscrews, surgical planning process, iterative surgical planning, spinalsurgery predictive modeling, and/or the like.

FIGS. 10A-10D are schematic diagrams illustrating an exampleembodiment(s) of an intraoperative tracking module and compatibilitythereof. In particular, as illustrated in FIGS. 10A and 10B, in someembodiments, an intraoperative tracking module and/or device 1002 can becompatible with one or more spinal implants, such as a spinal rod 1006and/or vertebral screw 1004. For example, as discussed in more detailbelow, in some embodiments, an intraoperative tracking module and/ordevice 1002 can comprise a hole or opening along a latitudinal axis ofthe tracking module and/or device that allows for insertion of a spinalrod 1006 therethrough.

In some embodiments, as illustrated in FIG. 10B, an intraoperativetracking device and/or module 1002 can be compatible with a tulip screw1004. In some embodiments, an intraoperative tracking device and/ormodule 1002 can be delivered separately from a vertebral screw 1004,such as a tulip screw 1004, or be delivered directly attached to orassociated to a vertebral screw 1004, such as a tulip screw 1004. Insome embodiments, an intraoperative tracking device and/or module 1002is delivered or otherwise provided sterile to the operating room. Insome embodiments, an intraoperative tracking device and/or module 1002comprises one or more latches or attachment mechanisms that allow thetracking module and/or device 1002 to attach to one or more protrusionsof a tulip screw 1004. For example, as illustrated in FIG. 10B, in someembodiments, an intraoperative tracking module and/or device 1002comprises two latches or attachment mechanisms or protrusions thatattach to two extensions or protrusions of a tulip screw 1004. In someembodiments, as illustrated in FIG. 10B, the two latches or attachmentmechanisms or protrusions of an intraoperative tracking device or module1002 are separated from each other by a distance substantially equal toa distance of separation between two protrusions of a tulip screw 1004to allow for insertion of a spinal rod 1006 therethrough.

In some embodiments, as illustrated in FIGS. 10C and 10D, anintraoperative tracking module or device 1002 can be compatible with oneor more instruments or surgical tools, such as a nut driver 1008 and/ora screw driver 1010. In particular, in some embodiments, anintraoperative tracking device and/or module 1002 can comprise anopening or hole along or substantially parallel to a vertical axis orlongitudinal axis of the module or device 1002 to allow insertion,rotation, and/or removal of a nut driver 1008 and/or screw driver 1010,such that a nut driver 1008 and/or screw driver 1010 can access avertebral screw 1004. In some embodiments, an intraoperative trackingdevice and/or module 1002 can comprise a first opening or hole along thevertical axis or longitudinal axis and a second opening or hole alongthe horizontal or latitudinal axis of the module or device 1002. In someembodiments, a longitudinal axis of the first opening or hole and alongitudinal axis of the second opening or hole can be substantiallyperpendicular.

Additional Features of Some Embodiments of Intraoperative TrackingModules

FIGS. 11A-11E illustrate an example embodiment(s) of an intraoperativetracking module and compatibility thereof. In some embodiments, anintraoperative tracking module or device 1102 can comprise one or moreelectronic components and/or sensors, such as for example a gyroscope,accelerometer, power source, wireless transmitter, data filter, anelectrical circuit, and/or the like.

As illustrated in FIGS. 11A-11E, in some embodiments, an intraoperativetracking module or device 1102 can comprise an opening, aperture, and/oraccess port 1110 at the top of the device that leads to a tunnel or holeor conduit along or substantially parallel to a vertical or longitudinalaxis of the module or device 1102 throughout the whole verticalthickness or height of the module or device 1102, for example connectinga top end and a bottom end of the intraoperative tracking module ordevice 1102. In some embodiments, such opening, aperture, and/or accessport 1110 at the top of the device and a vertical tunnel or conduitextending therefrom can allow insertion, rotation, and/or removal of oneor more surgical tools, such as a nut driver and/or screwdriver, andaccess to a vertebral screw 1104.

In some embodiments, an intraoperative tracking module and/or device1102 comprises an opening, aperture, tunnel, and/or hole 1108 along orsubstantially parallel to a horizontal or latitudinal axis of the deviceor module 1102 throughout the whole horizontal thickness or width of themodule or device 1102. In some embodiments, such opening, aperture,and/or tunnel and/or hole 1108 can be located near the bottom of themodule or device 1102, which can allow for insertion and/or placement ofa spinal rod 1106 therethrough. In some embodiments, the opening,aperture, hole, and/or tunnel 1108 along a horizontal or latitudinalaxis of the device or module 1102 can comprise a larger top section,comprising a larger cross-sectional area perpendicular to a horizontalor latitudinal axis of the device or module 1102, and a smaller bottomsection, comprising a smaller cross-sectional area perpendicular to ahorizontal or latitudinal axis of the device or module 1102. In someembodiments, the larger top section can facilitate and/or allow for easyinitial insertion of a spinal rod 1106, which can then be lowered intothe smaller bottom section to place the spinal rod 1106 within a tulipscrew 1104.

In some embodiments, a width, along a horizontal or latitudinal axis, ofthe larger top section of the opening 1108 can be about 7 mm, about 7.5mm, about 8 mm, about 8.5 mm, about 9 mm, about 9.5 mm, about 10 mm,about 10.5 mm, about 11 mm, about 11.5 mm, about 12 mm, and/or be withina range defined by two of the aforementioned values. In someembodiments, a width along a horizontal or latitudinal axis, of thesmaller bottom section of the opening 1108 can be about 4 mm, about 4.5mm, about 5 mm, about 5.5 mm, about 6 mm, about 6.5 mm, about 7 mm,about 7.5 mm, about 8 mm, about 8.5 mm, about 9 mm, and/or be within arange defined by two of the aforementioned values. In some embodiments,a width along a horizontal or latitudinal axis, of the smaller bottomsection of the opening 1108 can be substantially equal to a diameter aspinal rod 1106.

In some embodiments, an intraoperative tracking module and/or device1102 can comprise one or more protrusions or notches 1112, such as twoin the illustrated embodiments in FIGS. 11A-11E, proximate to the bottomof the device or module 1102 for attaching the module or device 1102 toone or more protrusions or extensions or notches of a tulip screw 1104.In some embodiments, the one or more protrusions or notches 1112 can bespaced apart from each other, thereby creating a gap or opening throughwhich a spinal rod 1112 can be placed. In some embodiments, the one ormore protrusions or notches 1112 can comprise a width that substantiallymatches the width of one or more protrusions or notches of a tulip screw1104. In addition, in some embodiments, the one or more protrusions ornotches 1112 can be configured to attach to a circular notch of a tulipscrew 1104, thereby providing stability when affixing the intraoperativetracking device or module 1102 to a tulip screw 1104.

In some embodiments, the one or more protrusions or notches 1112 cancomprise one or more grooves on the internal surface thereof tofacilitate attachment to a tulip screw. As such, in some embodiments,the bottom portion of an intraoperative tracking module or device 1102can comprise a discontinuous circumference. In some embodiments, the oneor more protrusions or notches 1112 can allow for each fixation and/orremoval and/or breakage of an intraoperative tracking device or module1102 from a tulip screw 1104. In some embodiments, the one or moreprotrusions or notches 1112 can comprise an arcuate shape or curvatureto substantially match an arcuate shape or curvature of one orprotrusions or notches of a tulip screw 1104.

FIGS. 12A-12E illustrate an example embodiment(s) of an intraoperativetracking module and compatibility thereof. The example embodiment(s)shown in FIGS. 12A-12E share some similar features with the exampleembodiment(s) shown in FIGS. 11A-11E. For example, some features inFIGS. 12A-12E with the same reference numbers as some features in FIGS.11A-11E can comprise similar or the same characteristics.

As illustrated in FIGS. 12A-12E, in some embodiments, an intraoperativetracking module or device 1202 comprises a strip 1204. In someembodiments, the strip 1204 can be located proximate to a top edge ofthe intraoperative tracking device or module 1202, as illustrated inFIGS. 12A-12E, or can be located at or near another portion of theintraoperative tracking device or module 1202.

In some embodiments, the strip 1204 can be configured to act as amechanism to turn on or power on the intraoperative tracking device ormodule 1204. For example, in some embodiments, a user or surgeon ormedical personnel can pull on the strip 1204 to remove the strip, whichcan thereby complete a power circuit inside the intraoperative trackingdevice or module 1204, thereby initiating tracking and/or collection ofdata by the intraoperative tracking device. As such, in someembodiments, the surgeon or other medical personnel may pull and removethe strip 1204 after a screw 1104 has been secured to a vertebra andafter an intraoperative tracking module or device 1202 is secured to thescrew 1104.

In some embodiments, the strip 1204 can comprise non-conductivematerial, such as for example plastic or paper. As such, in someembodiments, the strip 1204 in its original position may interfere withthe power circuit within the intraoperative tracking device or module1202, thereby preventing tracking of data and/or undesired use of power,as the power source within the intraoperative tracking device or module1202 may be limited. In some embodiments, once the strip 1204 isremoved, the power circuit within the intraoperative tracking device ormodule 1202 can be completed, thereby powering on the intraoperativetracking device or module 1202.

FIGS. 13A-13G illustrate an example embodiment(s) of an intraoperativetracking module and compatibility thereof. The example embodiment(s)shown in FIGS. 13A-13G share some similar features with the exampleembodiment(s) shown in FIGS. 11A-11E and/or FIGS. 12A-12E. For example,some features in FIGS. 13A-13G with the same reference numbers as somefeatures in FIGS. 11A-11E and/or FIGS. 12A-12E can comprise similar orthe same characteristics.

As illustrated in FIGS. 13A-13G, in some embodiments, an intraoperativetracking module or device 1302 can comprise one or more protrusions ornotches 1304, such as two in the illustrated embodiments in FIGS.13A-13G, proximate to the bottom of the device or module 1302 forattaching the module or device 1302 to one or more protrusions orextensions or notches of a tulip screw 1104. In some embodiments, theone or more protrusions or notches 1304 can be spaced apart from eachother, thereby creating a gap or opening through which a spinal rod 1112can be placed.

In some embodiments, one or more of the one or more protrusions ornotches 1304 can comprise a width that is narrower that the width of oneor more protrusions or notches of a tulip screw. For example, in theexample embodiment(s) illustrated in FIGS. 13A-13G, one edge of aprotrusion or notch 1304 can substantially match or line up with an edgeof a protrusion or notch of a tulip screw 1104, while another edge of aprotrusion or notch 1304 can end before another edge of a protrusion ornotch of a tulip screw 1104, thereby decreasing the area of overlapbetween a protrusion or notch 1304 and a protrusion or notch of a tulipscrew 1104. In some embodiments, having a protrusion or notch 1304 of anintraoperative tracking module or device 1302 that is narrower that aprotrusion or notch of a tulip screw 1104 can make it easier to breakoff or otherwise decouple the intraoperative tracking module or device1302 from the tulip screw 1104 while maintaining sufficient stability toaffix the intraoperative tracking module or device 1302 to the tulipscrew 1104.

In addition, in some embodiments, the one or more protrusions or notches1304 can be configured to attach to a horizontal notch of a tulip screw1104 which can be located higher than a circular notch of a tulip screw1104. In some embodiments, by allowing the one or more protrusions ornotches 1304 to attach to a higher and/or horizontal notch of a tulipscrew 1104, it can be easier to break off or otherwise decouple theintraoperative tracking module and/or device 1302 from a tulip screw1104 and/or provide less stress thereto, while still providingsufficient stability when affixing the intraoperative tracking device ormodule 1302 to a tulip screw 1104.

Similar to the example embodiment(s) illustrated in FIGS. 11A-11E, insome embodiments, the one or more protrusions or notches 1304 cancomprise one or more grooves on the internal surface thereof tofacilitate attachment to a tulip screw. As such, in some embodiments,the bottom portion of an intraoperative tracking module or device 1302can comprise a discontinuous circumference. In some embodiments, the oneor more protrusions or notches 1304 can allow for each fixation and/orremoval and/or breakage of an intraoperative tracking device or module1302 from a tulip screw 1104. In some embodiments, the one or moreprotrusions or notches 1304 can comprise an overall arcuate shape orcurvature to substantially match an arcuate shape or curvature of one orprotrusions or notches of a tulip screw 1104.

FIGS. 14A-14F illustrate an example embodiment(s) of an intraoperativetracking module and compatibility thereof. The example embodiment(s)shown in FIGS. 14A-14F share some similar features with the exampleembodiment(s) shown in FIGS. 11A-11E and/or FIGS. 12A-12E and/or FIGS.13A-13G. For example, some features in FIGS. 14A-14F with the samereference numbers as some features in FIGS. 11A-11E and/or FIGS. 12A-12Eand/or FIGS. 13A-13G can comprise similar or the same characteristics.

As illustrated in FIGS. 14A-14F, in some embodiments with a narrowernotch or protrusion 1304 proximate to the bottom end of anintraoperative tracking device or module 1402, the intraoperativetracking device or module 1402 can comprise a strip 1204, similar to theexample embodiment(s) illustrated in FIGS. 12A-12E. One or morecharacteristics and/or features of a strip 1204 in some embodiments witha narrower notch or protrusion 1304 proximate to the bottom end of anintraoperative tracking device or module 1402 can be similar to a strip1204 illustrated in the example embodiment(s) of FIGS. 12A-12E.

Additional Features of Some Embodiments of Intraoperative Tracking

FIG. 15 is a flowchart illustrating an example embodiment(s) ofintraoperative tracking and its role in developing patient-specificimplants, treatments, operations, and/or procedures. In addition, FIG.15 illustrates some embodiments of a surgical method and/or techniquefor applying intraoperative tracking.

As illustrated in FIG. 15 , in some embodiments, the systems, devices,and methods described herein can be configured to generate a plan forspinal surgery at block 1502, utilizing one or more planning featuresand/or predictive modeling features described herein. In someembodiments, the systems, devices, and methods described herein can beconfigured to then develop and/or manufacture a patient-specific spinalrod at block 1504 according to the predetermined surgical plan.

In some embodiments, a surgeon or other medical personnel can place oneor more screws, with or without an intraoperative tracking moduleattached to the screw, at block 1506. Additional details regarding screwplacement are discussed further in connection with FIG. 16 .

In some embodiments, a surgeon or other medical personnel can performinitial rod placement at block 1508. Further, in some embodiments, asurgeon or other medical personnel can initiate intraoperative trackingto assist in obtaining a final position of the rod at block 1508.Additional details regarding spinal rod insertion and/or finalization ofa spinal rod position are discussed further in connection with FIGS. 17,18A-18D, and 19 .

In some embodiments, the systems, devices, and methods described hereincan provide calculation of screw offset for one or more screws at block1510 to account for any offset in tracking data from an intraoperativetracking module or device. Additional details regarding screw offsetcalculation are discussed further in connection with FIG. 20 .

In some embodiments, a surgeon or other medical personnel can finalizethe rod placement and fixate one or more nuts at block 1512. In someembodiments, once a spinal rod has been fixed on a spine of a patient,one or more intraoperative tracking modules or devices used forintraoperative tracking can be discarded at block 1514. Additionaldetails regarding discarding one or more intraoperative tracking modulesor devices are discussed further in connection with FIG. 21 .

FIG. 16 is a schematic diagram illustrating an example embodiment(s) ofpositioning one or more spinal screws with an intraoperative trackingmodule and one or more spinal screws without an intraoperative trackingmodule on a spine during surgery. As illustrated in FIG. 16 , in someembodiments, one or more vertebral screws can be inserted into one ormore vertebrae 1600 according to a preoperatively determined surgicalplan. In particular, in some embodiments, one or more vertebral screws1604 attached to an intraoperative tracking module or device 1602 can beaffixed to one or more vertebrae 1600. For example, in some embodiments,one or more vertebral screws 1604 attached to an intraoperative trackingmodule or device 1602 can be affixed to one or more vertebrae 1600 ofinterest with extreme and/or substantial deformities. In addition, insome embodiments, one or more vertebral screws 1604 without anintraoperative tracking device or module 1602 attached thereto can beinserted and/or affixed to one or more other vertebrae 1600 and/or oninstrumented levels. In some embodiments, a preoperatively determinedsurgical plan can comprise one or more suggested vertebrae for attachingone or more intraoperative tracking modules or devices 1602. In someembodiments, a surgeon can decide which vertebrae to attach one or moreintraoperative tracking modules or devices 1602 either during or beforesurgery.

In some embodiments, the system can allow a surgeon to select via a userinterface any subset of intraoperative tracking modules or devices 1602attached to a vertebra for tracking purposes. For example, in someembodiments, a surgeon can request the system to track and/or provide anangle between two particular intraoperative tracking modules or devices1602 attached to two different vertebrae. In some embodiments, thesystem can automatically provide angular tracking data between twoparticular intraoperative tracking modules or devices 1602 attached totwo different vertebrae.

In some embodiments, an intraoperative tracking module or device 1602can be provided to a surgeon or medical personnel as being affixed to avertebral screw 1604. In some embodiments, a surgeon or medicalpersonnel can attach an intraoperative tracking module or device 1602 toa vertebral screw 1604 in the operation room.

FIG. 17 is a flowchart illustrating an example embodiment(s) of rodplacement and intraoperative tracking. As illustrated in FIG. 17 , insome embodiments, a surgeon or other medical personnel can initiallyinsert a spinal rod into one or more vertebral screw heads that areaffixed to one or more vertebrae at block 1702. In some embodiments, asurgeon or other medical personnel can launch a software of the systemat block 1704. In some embodiments, by launching the software, thesystem can generate and/or provide a user interface.

In some embodiments, the user interface can comprise and/or provide amenu to select one or more spinal segments. FIG. 18A illustrates ascreenshot 1802 of an example embodiment(s) of a user interface and/orsoftware platform for selecting one or more spinal segments. Asillustrated in FIG. 18A, in some embodiments, the user interface and/orsoftware platform can allow a user to select one or more of a lumbarsegment, which can comprise L1 to S1, a thoracic segment, which cancomprise T4 to T12, and/or a thoracolumbar segment, which can compriseT4 to S1. Referring back to FIG. 17 , in some embodiments, a user and/orsurgeon and/or other medical personnel can select one or more spinalsegments for performing intraoperative tracking at block 1706.

In some embodiments, the user interface can comprise and/or provide amenu to enter one or more patient measurements. FIG. 18B illustrates ascreenshot 1804 of an example embodiment(s) of a user interface and/orsoftware platform for entering one or more patient measurements. Asillustrated in FIG. 18B, in some embodiments, the user interface and/orsoftware platform can allow a user to enter one or more patientparameters. In some embodiments, the one or more patient parameters cancomprise one or more patient sagittal parameters and/or otherspinopelvic parameters, such as pelvic incidence (PI), pelvic tilt (PT),and/or lumbar lordosis (LL) of a preoperative state of the spine and/orof a preoperatively determined surgical plan. In some embodiments, theuser interface and/or software platform can allow a user to enter one ormore patient parameters and/or spinopelvic parameters of one or morespinal segments. Referring back to FIG. 17 , in some embodiments, a userand/or surgeon and/or other medical personnel can enter one or morepatient measurements and/or spinopelvic parameters at block 1708.

In some embodiments, the user interface can comprise and/or provide amenu to start or initiate one or more intraoperative tracking modules.FIG. 18C illustrates two screenshots 1806, 1808 of an exampleembodiment(s) of a user interface and/or software platform forinitiating or starting one or more intraoperative tracking modules. Asillustrated in FIG. 18C, in some embodiments, the user interface and/orsoftware platform can prompt a user to position one or moreintraoperative tracking modules or devices at one or more particularvertebrae, for example on S1 and/or L1. In some embodiments, the userinterface and/or software platform can prompt validation of thepositioning of the one or more intraoperative tracking modules ordevices. Referring back to FIG. 17 , in some embodiments, a user and/orsurgeon and/or other medical personnel can start or initiate one or moreintraoperative tracking modules at block 1710.

In some embodiments, the user interface can comprise and/or provide amenu for displaying intraoperative tracking in real-time, nearreal-time, and/or in substantially real-time. FIG. 18D illustrates ascreenshot 1810 of an example embodiment(s) of a user interface and/orsoftware platform for displaying intraoperative tracking. As illustratedin FIG. 18D, in some embodiments, the user interface and/or softwareplatform can provide a graphical display of intraoperative trackingdata, which can include one or more sagittal measurements, such as PI,LL, and/or PT during surgery. In some embodiments, the user interfaceand/or software platform can provide a graphical display of PI, LL,and/or PT taken from a preoperative state, from the preoperativelydetermined surgical plan, and/or current state as measured from one ormore intraoperative tracking devices or modules. In some embodiments,the user interface and/or software platform can provide a graphicaldisplay of PI, LL, and/or PT for each spinal segment. As such, in someembodiments, a surgeon can be aware of the current state of spinaladjustment and/or surgery and modify the surgical procedure to bettermatch the preoperatively determined surgical plan. Referring back toFIG. 17 , in some embodiments, a user and/or surgeon and/or othermedical personnel can be provided and/or follow intraoperative trackingdata at block 1712.

FIG. 19 is a schematic diagram illustrating an example embodiment(s) ofpositioning a rod during spinal surgery based on intraoperativetracking. As illustrated in FIG. 19 , in some embodiments, a surgeon canutilize intraoperative tracking data to facilitate positioning of aspinal rod during surgery. In particular, in some embodiments, a surgeoncan insert one or more nuts and compress and/or distract between one ormore vertebral screws to obtain the preoperative planned value byreferring to and/or with the assistance of intraoperative tracking.

Referring back to FIG. 15 , in some embodiments, the systems, devices,and methods described herein can provide calculation of screw offset forone or more screws at block 1510 to account for any offset in trackingdata from an intraoperative tracking module or device. FIG. 20 is aflowchart and/or schematic diagram illustrating an example embodiment(s)of calculating screw offset for intraoperative tracking. In someembodiments, screw offset calculation and/or one or more featuresthereof can be optional. In some embodiments, screw offset calculationand/or one or more features thereof can be performed only if and whennecessary.

In some embodiments, the systems, devices, and methods described hereincan comprise and/or be configured to calculate the offset between anendplate vertebra and a screw. For example, in some embodiments, theoffset between an endplate of a vertebra and a screw can be calculatedon a sagittal fluoroscopy. In some embodiments, calculating an offsetbetween an endplate vertebra and a screw can be advantageous, forexample if a screw is not substantially perpendicular to an endplate ofa vertebra. In some embodiments, such offset calculation can beadvantageous because the system can be configured to measure an anglebetween two vertebrae based on one or more intraoperative trackingmodules or devices attached to each of the two vertebrae.

In some embodiments, an offset calculation between an endplate vertebraand a screw may not be necessary for one or more vertebrae, for exampleif a screw is substantially perpendicular to an endplate of a vertebraand/or if it is assumed that a screw is substantially perpendicular toan endplate of a vertebra.

In some embodiments, as illustrated in FIG. 20 , a surgeon and/or othermedical personnel can take one or more medical images 2002 of a spine ofthe patient after attaching one or more intraoperative tracking devicesor modules and/or one or more vertebral screws, for example in theoperation room. In some embodiments, the one or more medical images 2002can comprise one or more x-ray images, one or more CT images, one ormore MRI images, and/or the like.

In some embodiments, the one or more medical images 2002 taken of aspine of the patient after attaching one or more intraoperative trackingdevices or modules and/or one or more vertebral screws can betransmitted to the system for calculating an offset between an endplatevertebra and a screw. In some embodiments, the one or more medicalimages 2002 taken of a spine of the patient after attaching one or moreintraoperative tracking devices or modules and/or one or more vertebralscrews can be displayed on a computer display as illustrated in FIG. 20. In some embodiments, a surgeon or other medical personnel can take aphotograph of the displayed one or more medical images 2002 by using atablet computing device 2004 or other computing device, which can thenact as an intermediary for transmitting the one or more medical images2002 to the system for calculating an offset between an endplatevertebra and a screw. That way, in some embodiments, it can be possibleto avoid any connectivity issues between the system and a medicalimaging display system in the operation room that displays the one ormore medical images 2002. In some embodiments, a software and/or userinterface operating on the tablet computing device 2004 or othercomputing device can generate and/or display and/or provide guidance toa user for taking an accurate photograph of the one or more medicalimages 2002 to ensure a certain level of accuracy.

In some embodiments, once the system receives the one or medical images2002, the system can be configured to focus on one or more regionsand/or one or more vertebrae 2006 shown in the one or more medicalimages 2002 to calculate an offset between an endplate of a vertebra anda screw. In some embodiments, as illustrated in the example image of2008, the system can be configured to identify a straight line at thecenter of a vertebral screw along and/or parallel to a longitudinal axisof the vertebral screw. In some embodiments, as illustrated in theexample image of 2008, the system can be configured to identify a lineextending from an edge or end of an endplate of a vertebra in which avertebral screw has been inserted. In some embodiments, the system canbe configured to automatically or semi-automatically determine oridentify a straight line at the center of a vertebral screw along and/orparallel to a longitudinal axis of the vertebral screw and/or a lineextending from an edge or end of an endplate of a vertebra in which avertebral screw has been inserted. In some embodiments, the system canbe configured to receive from a user a manual identification and/ordetermination of a straight line at the center of a vertebral screwalong and/or parallel to a longitudinal axis of the vertebral screwand/or a line extending from an edge or end of an endplate of a vertebrain which a vertebral screw has been inserted. In some embodiments, thesystem can be configured to identify as an offset angle between anendplate of a vertebra and a screw inserted therein, an angle between astraight line at the center of a vertebral screw along or parallel to alongitudinal axis of the vertebral screw and a line extending from anedge or end of an endplate of a vertebra in which a vertebral screw hasbeen inserted.

In some embodiments, based at least in part on the calculated offsetangle between an endplate of a vertebra and a screw inserted therein,the system can be configured to update and/or generate one or more anglecalculation taking into account the offset value. For example, in theillustrated example embodiment(s) 2010, the system can be configured togenerate and/or update one or more sagittal measurements based at leastin part on the calculated offset angle, such as for example PI, LL, PT,TK, and/or any other spinopelvic parameter.

Referring back to FIG. 15 , in some embodiments, once a spinal rod hasbeen finally fixated on a spine of a patient, whether or not an offsetangle has been taken into account, one or more intraoperative trackingmodules or devices used for intraoperative tracking can be discarded atblock 1514. FIG. 21 is a flowchart and/or schematic diagram illustratingan example embodiment(s) of discarding intraoperative tracking modulesand/or nuts after intraoperative tracking and/or finalization of rodplacement.

In some embodiments, one or more intraoperative tracking modules ordevices 2106 can be configured for single-use. In some embodiments, oneor more intraoperative tracking modules or devices can be configured formultiple uses. In some embodiments, once intraoperative tracking is nolonger needed, for example by obtaining a final position of a spinal rodand/or after completion of surgery, a surgeon or other medical personnelcan break off one or more nuts 2102 and remove one or moreintraoperative tracking modules or devices 2106 from the one or morevertebral screws.

In some embodiments, once intraoperative tracking is no longer needed,for example by obtaining a final position of a spinal rod and/or aftercompletion of surgery, a software operating on the system can turn offor power off one or more intraoperative tracking devices or modules tostop tracking. In some embodiments, once intraoperative tracking is nolonger needed, the surgeon or other medical personnel can remove one ormore intraoperative tracking devices or modules 2106 from one or morevertebral screws and discard them in an anti-wave bag. In someembodiments, once intraoperative tracking is no longer needed, thesurgeon or other medical personnel can break off one or more nuts 2102and discard them.

Additional Features of Some Embodiments of Intraoperative Tracking

FIG. 22 illustrates an example embodiment(s) of intra-operative trackingthat can be used in conjunction with PediGuard technology. Asillustrated in FIG. 22 , in some embodiments, an intraoperative trackingdevice or module 2202 can be configured to be attached to a screw 2204,such as a pedicle screw, and/or still allow use of a surgical tool 2206,such as a screwdriver, to be used while the intraoperative trackingdevice or module 2202 is attached to a screw 2204.

In some embodiments, systems, methods, and devices described herein canbe used in conjunction and/or in combination with PediGuard technology.In some embodiments, an intraoperative tracking device or module 2202can be configured to be used in conjunction with and/or in combinationwith PediGuard technology. In particular, in some embodiments, thesystems, devices, and methods described herein can provide anintelligent screw, which can measure, for example, angulation of thescrew, impedance measures, and/or the like. In some embodiments, ameasured angulation of the screw by the system can allow and/orfacilitate control of the correction. In some embodiments, an impedancemeasure determined by the system can allow and/or facilitate control ofthe screw positioning. In some embodiments, the system can comprise acannulated screw equipped with one or more intra-operative trackingdevices or systems. In some embodiments, one or more intra-operativetracking devices or systems can be assembled with a PediGuard device orsystem.

In some embodiments, an example method of using an intra-operativetracking system, device or module 2202 in conjunction with PediGuardtechnology can include one or more of the following processes: insertscrew with PediGuard to guide the screw; implement rod passing theintra-operative tracking device housing; and/or measure angulation by aninertial sensor(s) of the intra-operative tracking device, system,and/or technology.

FIGS. 23A-23B illustrates an example embodiment(s) of intra-operativetracking that can be used in conjunction with Choker technology. In someembodiments, Choker technology can be defined as the combination ofinstruments allowing the precise and controlled 3D correction of thespine applicable to all or some spinal conditions, including forexample, osteotomies, scoliosis, spondylolisthesis, trauma and/orassociated implants (monoaxial screws, connectors, transverse andlongitudinal bars specific to the Choker system) that can first serve asconnection point for the instruments during the correction maneuversand/or remain as internal stabilizers.

As illustrated in FIG. 23A, in some embodiments, Choker technology canbe used with an intra-operative tracking equipped screw(s) 2302 via thedevice and/or system housing, which can allow the system or measureangulation of osteotomy and reduction thereof. As illustrated in FIG.23B, in some embodiments, Choker technology can be used in conjunctionwith intra-operative tracking systems, devices, 2304 and methods hereindirectly, without screws with sensors in them, to measure angulation ofosteotomy and reduction thereof.

FIG. 24 illustrates an example embodiment(s) of intra-operative trackingthat can be used in conjunction with a surgical robot(s). As illustratedin FIG. 24 , in some embodiments, systems, devices, and methods forintra-operative tracking 2402 described herein can be used inconjunction with a surgical robot 2404. For example, in someembodiments, a surgical robot 2404 can be used in conjunction with ascrew(s) that comprises one or more intra-operative tracking sensors,devices, and/or systems 2402. In some embodiments, a surgical robot(s)2404 can be configured to place a screw comprising intra-operativetracking technology 2402 parallel to one or more endplates to avoidand/or minimize error of placement.

FIG. 25 illustrates an example embodiment(s) of intra-operative trackingthat can be used in conjunction with a surgical robot(s). As illustratedin FIG. 25 , in some embodiments, a surgical robot can be configured toutilize intra-operative tracking technology to perform one or moregestures and/or processes to finalize spinal correction application,such as compression and/or distraction of one or more screws. In someembodiments, the system can be configured to measure angulation, forexample using intra-operative tracking systems, devices, and/or methodsdescribed herein at block 2502. In some embodiments, the system can beconfigured to calculate a correction to apply at block 2504. In someembodiments, the system can be configured to apply the correction, forexample using a surgical robot(s) at block 2506.

In some embodiments, a surgical robot(s) operating in conjunction withintra-operative tracking systems, devices, and methods herein can beused for one or more of the following surgical steps: screw insertion toensure optimal positioning of a screw(s) with a vertebral endplate(s);insertion of rod, for example once screws are implanted; measuring oneor more spine angles; and/or applying a correction as needed. In someembodiments, a surgical robot(s) operating in conjunction withintra-operative tracking systems, devices, and methods herein cancomprise one or more sensors, such as for example inertial and/orpressure sensors.

Screw Planning

In some embodiments, systems, devices, and methods described herein areconfigured to develop, design, and/or plan patient-specific and/orsurgeon-specific spinal screws and/or other implants prior to surgery.In particular, in some embodiments, the systems, methods, and devicesdescribed herein can be configured to plan and/or designpatient-specific and/or surgeon-specific spinal screws, based onanalyzing one or more medical images of a patient for example, prior tosurgery, thereby decreasing the number of spinal screws that need toprepared for and be available during spinal surgery, for example in ascrew kit.

In some embodiments, the systems, methods, and devices described hereincan be configured to collect data, such as pre-operative data of a spineof a patient. For example, in some embodiments, the systems, methods,and devices can be configured to utilize a data collection protocol forscrew planning by analyzing one or more x-ray images, CT-scan images,and/or any other medical images of a patient. In some embodiments, basedon such data collected from one or more x-ray images, CT-scan images,and/or other medical images, the system can be configured to dynamicallyand/or automatically determine one or more desired lengths, diameters,and/or ranges thereof, of one or more screws for implantation in aspecific vertebra of a specific patient. In some embodiments, based ondata collected from one or more x-ray images, CT-scan images, and/orother medical images, the system can be configured to allow a user todetermine one or more desired lengths, diameters, and/or ranges thereof,of one or more screws for implantation in a specific vertebra of aspecific patient.

In some embodiments, based on such one or more desired lengths,diameters, and/or ranges thereof, whether determined automatically bythe system and/or with user input, the system can be configured to allowpicking out beforehand, prior to surgery, a patient-specific screw kitthat is tailored for that particular patient, which can includes one ormore screws that are determined to fit or likely fit the patient. Assuch, in some embodiments, the system can reduce the range of possiblescrews used by the surgeon during surgery and the size of the necessarystock to be maintained.

In some embodiments, the systems, methods, and/or devices describedherein can be configured to plan and/or design one or morepatient-specific and/or surgeon-specific vertebral screws forimplantation prior to spinal surgery based on one or more preoperativex-ray images, such as for example sagittal and/or coronal images, and/orone or more axial slices from one or more postoperative and/orintraoperative CT images of a spine of a patient. In some embodiments,the systems, devices, and/or methods described herein can be configuredto utilize one or more data collected from a data collection protocoland/or one or more patient information, such as for example genderand/or age, for planning and/or designing one or more patient-specificand/or surgeon-specific vertebral screws for implantation prior tospinal surgery.

In some embodiments, the systems, devices, and methods described hereincan be configured to collect and/or obtain data from one or morepreoperative and/or postoperative x-ray images by using one or moresagittal wizards to determine, for example, one or more of a heightand/or length of a vertebra, a diameter of an implanted screw, and/or adistance between a screw and a vertebra. In some embodiments, thesystems, devices, and methods described herein can be configured tocollect and/or obtain data from one or more preoperative and/orpostoperative x-ray images by using one or more coronal wizards todetermine, for example, one or more Cobb angles, slope of a vertebra,and/or distance between one or more parts of screws. In someembodiments, the systems, devices, and methods described herein can beconfigured to collect and/or obtain one or more other anatomicalmeasurements from one or more sagittal and/or coronal x-ray images.

In some embodiments, the systems, methods, and/or devices describedherein that utilize one or more axial slices from one or morepostoperative and/or intraoperative CT images can be configured todetermine an angle between the axis of a vertebral screw and an axis ofa vertebra to which a screw is attached to. In some embodiments, thesystems, methods, and/or devices described herein can be configured toanalyze one or more preoperative CT scans and/or one or more axialslices thereof to determine the length and/or a diameter of a screw forsurgical planning purposes prior to surgery. In some embodimentsutilizing one or more CT scans, the systems, devices, and methodsdescribed herein can be configured to utilize 3D reconstruction.

In some embodiments, the systems, devices, and methods described herein,whether utilizing one or more sagittal and/or coronal and/or frontalx-ray images and/or CT images, can be configured to utilize one or moredata collected from one or more medical images for screw planningpurposes.

In some embodiments, the systems, devices, and/or methods describedherein are configured to generate and/or develop a screw planning memofor a surgeon prior to spinal surgery. In particular, in someembodiments, the systems, devices, and/or methods described herein canbe configured to gather information obtained during the analysis togenerate a screw planning memo. In some embodiments, the systems,devices, and/or methods described herein can be configured to assistwith selecting and/or developing one or more vertebral screws in advanceof spinal surgery and/or allow reducing an inventory thereof prior tospinal surgery. In some embodiments, the systems, devices, and/ormethods described herein can be configured to be implemented using oneor more computer systems, which can be coupled to a network and/orinclude one or more internal and/or external data sources.

In particular, in some embodiments, the systems, methods, and devicescan be configured to obtain necessary data, such as one or moreanatomical measurements, from or more pre-operative x-ray images of thespine of a patient. For example, in some embodiments, the system can beconfigured to use one or more sagittal and/or coronal and/or frontalx-ray images and/or wizards as illustrated in FIGS. 26A-26B. FIGS.26A-26B illustrate an example(s) of a preoperative spinal x-ray image(s)that can be used for one or more embodiments described herein, such asfor example relating to screw planning, predictive modeling, and/orintraoperative tracking.

In some embodiments, the system can utilize a “screw wizard,” that canallow a user to perform one or more screw planning processes and/ormeasurements, such as any of those described herein, without necessarilygoing through any three-dimensional reconstruction. In some embodiments,one or more or all measurements can be taken on every instrumentedvertebra pre-operatively and/or post-operatively.

FIGS. 27A-27C illustrate an example(s) of a preoperative sagittal spinalx-ray image(s) that can be used for one or more embodiments describedherein. FIGS. 27A-27C illustrate an example(s) of a preoperativesagittal spinal x-ray image(s) that can be used for one or moreembodiments described herein, such as for example relating to screwplanning, predictive modeling, and/or intraoperative tracking.

In particular, in some embodiments, by analyzing one or morepre-operative sagittal x-ray images, the system can be configured toand/or utilized to determine an anterior height of a vertebra asillustrated in the example in FIG. 27A. In some embodiments, byanalyzing one or more pre-operative sagittal x-ray images, the systemcan be configured to and/or be utilized to determine a posterior heightof the vertebra as illustrated in the example in FIG. 27B. In someembodiments, by analyzing one or more pre-operative sagittal x-rayimages, the system can be configured to and/or be utilized to determinean upper length of the vertebra as illustrated in the example of FIG.27C.

FIGS. 28A-28D illustrate an example(s) of a preoperative coronal spinalx-ray image(s) that can be used for one or more embodiments describedherein. FIGS. 28A-28D illustrate an example(s) of a preoperative coronalspinal x-ray image(s) that can be used for one or more embodimentsdescribed herein, such as for example relating to screw planning,predictive modeling, and/or intraoperative tracking.

In particular, in some embodiments, by analyzing one or morepre-operative coronal x-ray images, the system can be configured toand/or utilized to determine one or more Cobb angles, such as forexample levels, angles, and/or side of a deformity. In some embodiments,by analyzing one or more pre-operative coronal x-ray images, the systemcan be configured to and/or be utilized to determine an upper width ofthe vertebra as illustrated in the example of FIG. 28A. In someembodiments, by analyzing one or more pre-operative coronal x-rayimages, the system can be configured to and/or be utilized to determinea slope of the vertebra, such as for example an angle between the upperendplate of the vertebra and a horizontal line, as illustrated in theexample of FIG. 28B. In some embodiments, by analyzing one or morepre-operative coronal x-ray images, the system can be configured toand/or be utilized to determine a distance between two pedicles, such asfor example as measured from the center, as illustrated in the exampleof FIG. 28C. In some embodiments, by analyzing one or more pre-operativecoronal x-ray images, the system can be configured to and/or be utilizedto determine a distance between a pedicle and a right and/or left edgeof a vertebra as illustrated in the example of FIG. 28D.

In some embodiments, the systems, methods, and devices can be configuredto collect a list of screws that were previously used in prior cases,for example by a particular surgeon(s) for use in screw planning forfuture cases. In some embodiments, the systems, methods, and devices canbe configured to identify which screw(s) was used at which level byanalyzing and/or taking additional measurements on one or morepost-operative x-ray images of the spine of a patient from previouscases.

FIGS. 29A-29E illustrate an example(s) of a postoperative sagittalspinal x-ray image(s) that can be used for one or more embodimentsdescribed herein. FIGS. 29A-29E illustrate an example(s) of apostoperative sagittal spinal x-ray image(s) that can be used for one ormore embodiments described herein, such as for example relating to screwplanning, predictive modeling, and/or intraoperative tracking.

In some embodiments, by analyzing one or more post-operative sagittalx-ray images, the system can be configured to and/or be utilized todetermine a length(s) of an implanted screw(s) as illustrated in theexample of FIG. 29A. In some embodiments, by analyzing one or morepost-operative sagittal x-ray images, the system can be configured toand/or be utilized to determine a diameter(s) of an implanted screw(s)as illustrated in the example of FIG. 29B. In some embodiments, byanalyzing one or more post-operative sagittal x-ray images, the systemcan be configured to and/or be utilized to determine an angle(s) betweenan implanted screw(s) and an upper endplate of a vertebra as illustratedin the example of FIG. 29C. In some embodiments, by analyzing one ormore post-operative sagittal x-ray images, the system can be configuredto and/or be utilized to determine a distance between the anteriorextremity of a screw(s) and an anterior wall of a vertebra asillustrated in the example of FIG. 29D. In some embodiments, byanalyzing one or more post-operative sagittal x-ray images, the systemcan be configured to and/or be utilized to determine a distance betweena posterior wall of the vertebra and a posterior extremity of ascrew(s), such as for example the body of the screw without the head, asillustrated in the example of FIG. 29E.

FIGS. 30A-30B illustrate an example(s) of a postoperative coronal spinalx-ray image(s) that can be used for one or more embodiments describedherein. FIGS. 30A-30B illustrate an example(s) of a postoperativecoronal spinal x-ray image(s) that can be used for one or moreembodiments described herein, such as for example relating to screwplanning, predictive modeling, and/or intraoperative tracking.

In some embodiments, by analyzing one or more post-operative coronalx-ray images, the system can be configured to and/or utilized todetermine a distance between both anterior extremities of a screw(s) asillustrated in the example of FIG. 30A. In some embodiments, byanalyzing one or more post-operative coronal x-ray images, the systemcan be configured to and/or utilized to determine a distance between twoconnectors or head of a screw(s) as illustrated in the example of FIG.30B.

FIG. 31 illustrates an example(s) of a postoperative and/orintraoperative CT scan that can be used for one or more embodimentsdescribed herein. FIG. 31 illustrates an example(s) of a postoperativeand/or intraoperative CT image that can be used for one or moreembodiments described herein, such as for example relating to screwplanning, predictive modeling, and/or intraoperative tracking.

In particular, in some embodiments, the systems, methods, and devicescan be configured to utilize one or more post-operative orintra-operative CT scans when available in screw planning. Inparticular, in some embodiments, the system can be configured to utilizeone or more slices from a CT scan, such as for example axial slices,taken pre-operatively, post-operatively, and/or intra-operatively todetermine and/or use to determine an angle between the axis of ascrew(s) and the vertebra axis as illustrated in the example of FIG. 31.

As noted above, in some embodiments, the system can be configured toutilize one or more slices from a pre-operative CT scan. Further, insome embodiments, the system can be configured to render athree-dimensional model or rendering of the vertebra, which may or maynot include an implanted screw, for example by performingthree-dimensional reconstruction based on the CT-scan images.Furthermore, in some embodiments, screw planning can be useful and/oradvantageous for assessing the angle between the screw and vertebra, aswell as for determining the screw length and/or diameter. For example,in some embodiments, the system can be configured to determine and/orpredict a desired length and/or diameter, and/or one or more rangesthereof, of a screw for a particular vertebra, based on analysis of oneor more CT-scan images.

In some embodiments, the systems, methods, and devices can be configuredto utilize one or more patient information in screw planning. Forexample, in some embodiments, the system can be configured to analyzeone or more previous cases based on certain patient information to usein developing screw planning for a particular patient. In someembodiments, patient information can comprise sex, age, and/or height ofa patient.

In some embodiments, the systems, methods, and devices can be configuredto generate a screw planning memo for a surgeon based in part on theinformation and analysis conducted by the system as described herein.

FIGS. 32A-32G illustrate an example embodiment(s) of a screw planningmemo(s), such as for example based on one or more CT scans and/or x-rayimages of a patient. As illustrated in FIGS. 32A-32G, in someembodiments, the system can be configured to generate a screw planningmemo for a surgeon. In some embodiments, the screw planning memo can befor a particular segment of a spine of patient, such as for L1-L5 inFIGS. 32A-32G. In some embodiments, the screw planning memo can compriseone or more sectional images for each vertebra, such as for exampleL1-L5 in FIGS. 32A-32G, and can provide information relating to themaximum diameter and/or length of a left and/or right screw that can beinserted into each vertebral level. As illustrated in FIG. 32G, in someembodiments, the system can be configured to generate a summary of thediameter and/or length of one or more vertebral screws for insertinginto one or more vertebrae of the spinal section of interest, such asfor example L1-L5. In some embodiments, based on the screw planningmemo, the surgeon and/or other medical personnel can be provided a kitcomprising screws with a diameter and/or length that is within about 1%,about 2%, about 3% about 4%, about 5%, about 6%, about 7%, about 8%,about 9%, and/or about 10% of the determined diameter and/or length ofone or more vertebral screws for inserting into one or more vertebrae ofthe spinal section of interest, such as for example L1-L5. In someembodiments, based on the screw planning memo, the surgeon and/or othermedical personnel can be provided a kit comprising screws with adiameter and/or length that is within a range defined by two of theaforementioned values.

FIGS. 33A-33K illustrate an example embodiment(s) of a screw planningmemo(s), such as for example based on one or more CT scans for aparticular surgeon and/or patient. As illustrated in FIGS. 33A-33K, insome embodiments, the system can be configured to generate a screwplanning memo for a surgeon. In some embodiments, the screw planningmemo can be for a particular segment of a spine of patient, such as forT10-Iliac or T10 to S1 in FIGS. 33A-33K. In some embodiments, the screwplanning memo can comprise one or more sectional images for eachvertebra, such as for example T10 to S1 in FIGS. 33A-33K, and canprovide information relating to the maximum diameter and/or length of aleft and/or right screw that can be inserted into each vertebral level.As illustrated in FIG. 33K, in some embodiments, the system can beconfigured to generate a summary of the diameter and/or length of one ormore vertebral screws for inserting into one or more vertebrae of thespinal section of interest, such as for example T10 to S1. In someembodiments, based on the screw planning memo, the surgeon and/or othermedical personnel can be provided a kit comprising screws with adiameter and/or length that is within about 1%, about 2%, about 3% about4%, about 5%, about 6%, about 7%, about 8%, about 9%, and/or about 10%of the determined diameter and/or length of one or more vertebral screwsfor inserting into one or more vertebrae of the spinal section ofinterest, such as for example T10 to S1. In some embodiments, based onthe screw planning memo, the surgeon and/or other medical personnel canbe provided a kit comprising screws with a diameter and/or length thatis within a range defined by two of the aforementioned values.

System

FIG. 34 is a schematic diagram illustrating an embodiment of a systemfor developing patient-specific spinal treatments, operations, andprocedures. In some embodiments, a main server system 3402 may comprisean image analysis module 3404, a case simulation module 3406, anintra-operative tracking module 3408, a data utilization module 3410, apredictive modeling module 3428, a plan database 3412, an operationdatabase 3414, a surgeon database 3416, and/or a literature database3418. The main server system can be connected to a network 3420. Thenetwork can be configured to connect the main server to one or moreimplant production facility systems 3426, one or more medical facilityclient systems 3422, and/or one or more user access point systems 3424.

The image analysis module 3404 may function by providing image analysisand/or related functions as described herein. The case simulation module3406 may function by performing surgical planning, case simulation,and/or related functions as described herein. The intra-operativetracking module 3408 may function by performing intra-operative trackingand/or related functions as described herein. The data utilizationmodule 3410 may function by retrieving and/or storing data from and toone or more databases and/or related functions as described herein. Thepredictive modeling module 3428 may function by performing one or morepredictive modeling processes as described herein.

The plan database 3412 may provide a collection of all plans that havebeen generated by the system and/or related data. The operation database3414 may provide a collection of all surgical operations that have beenperformed utilizing the system and/or related data. The surgeon database3416 may provide a collection of all surgeons who have utilized thesystem and/or related data, such as surgeon preferences, skill levels,or the like. The literature database 3418 may provide a collection ofscientific literature related to spinal surgery.

Computer System

In some embodiments, the systems, processes, and methods describedherein are implemented using a computing system, such as the oneillustrated in FIG. 35 . The example computer system 3502 is incommunication with one or more computing systems 3520 and/or one or moredata sources 3522 via one or more networks 3518. While FIG. 35illustrates an embodiment of a computing system 3502, it is recognizedthat the functionality provided for in the components and modules ofcomputer system 3502 may be combined into fewer components and modules,or further separated into additional components and modules.

The computer system 3502 can comprise a patient-specific spinaltreatment, operations, and procedures module 3514 that carries out thefunctions, methods, acts, and/or processes described herein. Thepatient-specific spinal treatment, operations, and procedures module3514 is executed on the computer system 3502 by a central processingunit 3506 discussed further below.

In general the word “module,” as used herein, refers to logic embodiedin hardware or firmware or to a collection of software instructions,having entry and exit points. Modules are written in a program language,such as JAVA, C or C++, PYPHON or the like. Software modules may becompiled or linked into an executable program, installed in a dynamiclink library, or may be written in an interpreted language such asBASIC, PERL, LUA, or Python. Software modules may be called from othermodules or from themselves, and/or may be invoked in response todetected events or interruptions. Modules implemented in hardwareinclude connected logic units such as gates and flip-flops, and/or mayinclude programmable units, such as programmable gate arrays orprocessors.

Generally, the modules described herein refer to logical modules thatmay be combined with other modules or divided into sub-modules despitetheir physical organization or storage. The modules are executed by oneor more computing systems, and may be stored on or within any suitablecomputer readable medium, or implemented in-whole or in-part withinspecial designed hardware or firmware. Not all calculations, analysis,and/or optimization require the use of computer systems, though any ofthe above-described methods, calculations, processes, or analyses may befacilitated through the use of computers. Further, in some embodiments,process blocks described herein may be altered, rearranged, combined,and/or omitted.

The computer system 3502 includes one or more processing units (CPU)3506, which may comprise a microprocessor. The computer system 3502further includes a physical memory 3510, such as random access memory(RAM) for temporary storage of information, a read only memory (ROM) forpermanent storage of information, and a mass storage device 3504, suchas a backing store, hard drive, rotating magnetic disks, solid statedisks (SSD), flash memory, phase-change memory (PCM), 3D XPoint memory,diskette, or optical media storage device. Alternatively, the massstorage device may be implemented in an array of servers. Typically, thecomponents of the computer system 3502 are connected to the computerusing a standards based bus system. The bus system can be implementedusing various protocols, such as Peripheral Component Interconnect(PCI), Micro Channel, SCSI, Industrial Standard Architecture (ISA) andExtended ISA (EISA) architectures.

The computer system 3502 includes one or more input/output (I/O) devicesand interfaces 3512, such as a keyboard, mouse, touch pad, and printer.The I/O devices and interfaces 3512 can include one or more displaydevices, such as a monitor, that allows the visual presentation of datato a user. More particularly, a display device provides for thepresentation of GUIs as application software data, and multi-mediapresentations, for example. The I/O devices and interfaces 3512 can alsoprovide a communications interface to various external devices. Thecomputer system 3502 may comprise one or more multi-media devices 3508,such as speakers, video cards, graphics accelerators, and microphones,for example.

The computer system 3502 may run on a variety of computing devices, suchas a server, a Windows server, a Structure Query Language server, a UnixServer, a personal computer, a laptop computer, and so forth. In otherembodiments, the computer system 3502 may run on a cluster computersystem, a mainframe computer system and/or other computing systemsuitable for controlling and/or communicating with large databases,performing high volume transaction processing, and generating reportsfrom large databases. The computing system 3502 is generally controlledand coordinated by an operating system software, such as z/OS, Windows,Linux, UNIX, BSD, SunOS, Solaris, MacOS, or other compatible operatingsystems, including proprietary operating systems. Operating systemscontrol and schedule computer processes for execution, perform memorymanagement, provide file system, networking, and I/O services, andprovide a user interface, such as a graphical user interface (GUI),among other things.

The computer system 3502 illustrated in FIG. 35 is coupled to a network3518, such as a LAN, WAN, or the Internet via a communication link 3516(wired, wireless, or a combination thereof). Network 3518 communicateswith various computing devices and/or other electronic devices. Network3518 is communicating with one or more computing systems 3520 and one ormore data sources 3522. The patient-specific spinal treatment,operations, and procedures module 3514 may access or may be accessed bycomputing systems 3520 and/or data sources 3522 through a web-enableduser access point. Connections may be a direct physical connection, avirtual connection, and other connection type. The web-enabled useraccess point may comprise a browser module that uses text, graphics,audio, video, and other media to present data and to allow interactionwith data via the network 3518.

Access to the patient-specific spinal treatment, operations, andprocedures module 3514 of the computer system 3502 by computing systems3520 and/or by data sources 3522 may be through a web-enabled useraccess point such as the computing systems' 3520 or data source's 3522personal computer, cellular phone, smartphone, laptop, tablet computer,e-reader device, audio player, or other device capable of connecting tothe network 3518. Such a device may have a browser module that isimplemented as a module that uses text, graphics, audio, video, andother media to present data and to allow interaction with data via thenetwork 3518.

The output module may be implemented as a combination of an all-pointsaddressable display such as a cathode ray tube (CRT), a liquid crystaldisplay (LCD), a plasma display, or other types and/or combinations ofdisplays. The output module may be implemented to communicate with inputdevices 3512 and they also include software with the appropriateinterfaces which allow a user to access data through the use of stylizedscreen elements, such as menus, windows, dialogue boxes, tool bars, andcontrols (for example, radio buttons, check boxes, sliding scales, andso forth). Furthermore, the output module may communicate with a set ofinput and output devices to receive signals from the user.

The input device(s) may comprise a keyboard, roller ball, pen andstylus, mouse, trackball, voice recognition system, or pre-designatedswitches or buttons. The output device(s) may comprise a speaker, adisplay screen, a printer, or a voice synthesizer. In addition a touchscreen may act as a hybrid input/output device. In another embodiment, auser may interact with the system more directly such as through a systemterminal connected to the score generator without communications overthe Internet, a WAN, or LAN, or similar network.

In some embodiments, the system 3502 may comprise a physical or logicalconnection established between a remote microprocessor and a mainframehost computer for the express purpose of uploading, downloading, orviewing interactive data and databases on-line in real time. The remotemicroprocessor may be operated by an entity operating the computersystem 3502, including the client server systems or the main serversystem, and/or may be operated by one or more of the data sources 3522and/or one or more of the computing systems 3520. In some embodiments,terminal emulation software may be used on the microprocessor forparticipating in the micro-mainframe link.

In some embodiments, computing systems 3520 who are internal to anentity operating the computer system 3502 may access thepatient-specific spinal treatment, operations, and procedures module3514 internally as an application or process run by the CPU 3506.

The computing system 3502 may include one or more internal and/orexternal data sources (for example, data sources 3522). In someembodiments, one or more of the data repositories and the data sourcesdescribed above may be implemented using a relational database, such asDB2, Sybase, Oracle, CodeBase, and Microsoft® SQL Server as well asother types of databases such as a flat-file database, an entityrelationship database, and object-oriented database, and/or arecord-based database.

The computer system 3502 may also access one or more databases 3522. Thedatabases 3522 may be stored in a database or data repository. Thecomputer system 3502 may access the one or more databases 3522 through anetwork 3518 or may directly access the database or data repositorythrough I/O devices and interfaces 3512. The data repository storing theone or more databases 3522 may reside within the computer system 3502.

In some embodiments, one or more features of the systems, methods, anddevices described herein can utilize a URL and/or cookies, for examplefor storing and/or transmitting data or user information. A UniformResource Locator (URL) can include a web address and/or a reference to aweb resource that is stored on a database and/or a server. The URL canspecify the location of the resource on a computer and/or a computernetwork. The URL can include a mechanism to retrieve the networkresource. The source of the network resource can receive a URL, identifythe location of the web resource, and transmit the web resource back tothe requestor. A URL can be converted to an IP address, and a DomainName System (DNS) can look up the URL and its corresponding IP address.URLs can be references to web pages, file transfers, emails, databaseaccesses, and other applications. The URLs can include a sequence ofcharacters that identify a path, domain name, a file extension, a hostname, a query, a fragment, scheme, a protocol identifier, a port number,a username, a password, a flag, an object, a resource name and/or thelike. The systems disclosed herein can generate, receive, transmit,apply, parse, serialize, render, and/or perform an action on a URL.

A cookie, also referred to as an HTTP cookie, a web cookie, an internetcookie, and a browser cookie, can include data sent from a websiteand/or stored on a user's computer. This data can be stored by a user'sweb browser while the user is browsing. The cookies can include usefulinformation for websites to remember prior browsing information, such asa shopping cart on an online store, clicking of buttons, logininformation, and/or records of web pages or network resources visited inthe past. Cookies can also include information that the user enters,such as names, addresses, passwords, credit card information, etc.Cookies can also perform computer functions. For example, authenticationcookies can be used by applications (for example, a web browser) toidentify whether the user is already logged in (for example, to a website). The cookie data can be encrypted to provide security for theconsumer. Tracking cookies can be used to compile historical browsinghistories of individuals. Systems disclosed herein can generate and usecookies to access data of an individual. Systems can also generate anduse JSON web tokens to store authenticity information, HTTPauthentication as authentication protocols, IP addresses to tracksession or identity information, URLs, and the like.

Additional Embodiments

Although the embodiments discussed herein generally relate topatient-specific spinal treatment, operations, and procedures, thesystems, methods, and devices disclosed herein can be used for anynon-spinal patient-specific treatment, operations, and procedure aswell. Also, the systems, methods, and devices disclosed herein can beused with x-ray, Mill, CT, or any other imaging systems or devices thatproduce two-dimensional and/or three-dimensional medical image or videodata.

Although this invention has been disclosed in the context of certainembodiments and examples, it will be understood by those skilled in theart that the invention extends beyond the specifically disclosedembodiments to other alternative embodiments and/or uses of theinvention and obvious modifications and equivalents thereof. Inaddition, while several variations of the embodiments of the inventionhave been shown and described in detail, other modifications, which arewithin the scope of this invention, will be readily apparent to those ofskill in the art based upon this disclosure. It is also contemplatedthat various combinations or sub-combinations of the specific featuresand aspects of the embodiments may be made and still fall within thescope of the invention. It should be understood that various featuresand aspects of the disclosed embodiments can be combined with, orsubstituted for, one another in order to form varying modes of theembodiments of the disclosed invention. Any methods disclosed hereinneed not be performed in the order recited. Thus, it is intended thatthe scope of the invention herein disclosed should not be limited by theparticular embodiments described above.

Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments include, while other embodiments do not include, certainfeatures, elements and/or steps. Thus, such conditional language is notgenerally intended to imply that features, elements and/or steps are inany way required for one or more embodiments or that one or moreembodiments necessarily include logic for deciding, with or without userinput or prompting, whether these features, elements and/or steps areincluded or are to be performed in any particular embodiment. Theheadings used herein are for the convenience of the reader only and arenot meant to limit the scope of the inventions or claims.

Further, while the methods and devices described herein may besusceptible to various modifications and alternative forms, specificexamples thereof have been shown in the drawings and are hereindescribed in detail. It should be understood, however, that theinvention is not to be limited to the particular forms or methodsdisclosed, but, to the contrary, the invention is to cover allmodifications, equivalents, and alternatives falling within the spiritand scope of the various implementations described and the appendedclaims. Further, the disclosure herein of any particular feature,aspect, method, property, characteristic, quality, attribute, element,or the like in connection with an implementation or embodiment can beused in all other implementations or embodiments set forth herein. Anymethods disclosed herein need not be performed in the order recited. Themethods disclosed herein may include certain actions taken by apractitioner; however, the methods can also include any third-partyinstruction of those actions, either expressly or by implication. Theranges disclosed herein also encompass any and all overlap, sub-ranges,and combinations thereof. Language such as “up to,” “at least,” “greaterthan,” “less than,” “between,” and the like includes the number recited.Numbers preceded by a term such as “about” or “approximately” includethe recited numbers and should be interpreted based on the circumstances(e.g., as accurate as reasonably possible under the circumstances, forexample ±5%, ±10%, ±15%, etc.). For example, “about 3.5 mm” includes“3.5 mm.” Phrases preceded by a term such as “substantially” include therecited phrase and should be interpreted based on the circumstances(e.g., as much as reasonably possible under the circumstances). Forexample, “substantially constant” includes “constant.” Unless statedotherwise, all measurements are at standard conditions includingtemperature and pressure.

As used herein, a phrase referring to “at least one of” a list of itemsrefers to any combination of those items, including single members. Asan example, “at least one of: A, B, or C” is intended to cover: A, B, C,A and B, A and C, B and C, and A, B, and C. Conjunctive language such asthe phrase “at least one of X, Y and Z,” unless specifically statedotherwise, is otherwise understood with the context as used in generalto convey that an item, term, etc. may be at least one of X, Y or Z.Thus, such conjunctive language is not generally intended to imply thatcertain embodiments require at least one of X, at least one of Y, and atleast one of Z to each be present.

What is claimed is:
 1. A computer-implemented method for generating andassisting patient-specific spinal treatment, the method comprising:analyzing, using a computer system, one or more preoperative medicalimages of a spine of a patient to determine one or more preoperativespinopelvic parameters, wherein the one or more spinopelvic parameterscomprise one or more of lumbar lordosis (LL), preoperative thoracickyphosis (TK), pelvic incidence (PI), pelvic tilt (PT), or sagittalvertical axis (SVA) for one or more vertebrae; transforming, using thecomputer system, the determined one or more preoperative spinopelvicparameters to obtain one or more preoperative spinopelvic parameters ina frequency domain, wherein the transforming comprises applying aFourier transformation to the determined one or more preoperativespinopelvic parameters; filtering, using the computer system, the one ormore preoperative spinopelvic parameters in the frequency domain,wherein the filtering comprises filtering out one or more of the one ormore preoperative spinopelvic parameters in the frequency domaincomprising a frequency level above a predetermined threshold; applying,using the computer system, one or more predictive models to generate apredicted surgical outcome in the frequency domain based at least inpart on the filtered one or more preoperative spinopelvic parameters inthe frequency domain and one or more preoperative non-imaging datainputs of the patient, wherein the one or more predictive modelscomprises one or more of a generative adversarial network (GAN)algorithm, convolutional neural network (CNN) algorithm, or recurrentneural network (RNN) algorithm; and transforming, using the computersystem, the generated predicted surgical outcome in the frequency domainto obtain a generated predictive surgical outcome in a spatial domain,wherein the transforming the generated predicted surgical outcome in thefrequency domain comprises applying an inverse Fourier transformation tothe generated predicted surgical outcome in the frequency domain,generating, using the computer system, a patient-specific spinaltreatment based at least in part on the generated predictive surgicaloutcome in the spatial domain, wherein the generated patient-specificspinal treatment comprises one or more patient-specific spinal surgicalprocedures; attaching one or more intraoperative tracking modules to oneor more vertebral implants for implanting to one or more vertebrae ofinterest during spinal surgery of the patient, wherein the one or moreintraoperative tracking modules comprise at least one sensor and a stripfor blocking a power circuit within the one or more intraoperativetracking modules; removing the strip from the one or more intraoperativetracking modules to initiate tracking of one or more angles between theone or more vertebrae to which the one or more intraoperative trackingmodules are attached to; and generating, by the computer system,intraoperative tracking data in real-time and comparing the generatedtracking data in real-time to the generated one or more patient-specificspinal surgical procedures to assist the generated patient-specificspinal treatment, wherein the computer system comprises a computerprocessor and an electronic storage medium.
 2. The computer-implementedmethod of claim 1, wherein the one or more spinopelvic parameters aredetermined automatically by the computer system.
 3. Thecomputer-implemented method of claim 1, wherein the one or morepreoperative medical images of the spine of the patient comprise one ormore sagittal x-ray images and one or more frontal x-ray images.
 4. Thecomputer-implemented method of claim 1, wherein the generated predictivesurgical outcome in the spatial domain comprises one or morepostoperative spinopelvic parameters.
 5. The computer-implemented methodof claim 1, wherein the generated patient-specific spinal treatmentfurther comprises one or more specifications of a spinal rod to beimplanted to the spine of the patient.
 6. The computer-implementedmethod of claim 1, wherein the at least one sensor is chosen from thegroup comprising: accelerometers and/or gyroscopes.
 7. Thecomputer-implemented method of claim 1, wherein the one or morevertebral implants comprise one or more vertebral screws.
 8. Thecomputer-implemented method of claim 7, wherein the one or morevertebral screws comprise one or more tulip screws.
 9. Thecomputer-implemented method of claim 7, wherein the one or moreintraoperative tracking modules comprises one or more notches configuredto attach or remove the one or more intraoperative tracking modules tothe one or more vertebral screws.
 10. The computer-implemented method ofclaim 7, wherein the one or more intraoperative tracking modulescomprises a first conduit adapted to allow insertion of a surgical tool,and wherein the one or more intraoperative tracking modules comprises asecond conduit adapted to allow insertion of a spinal rod.
 11. Thecomputer-implemented method of claim 10, wherein a longitudinal axis ofthe first conduit is substantially perpendicular to a longitudinal axisof the second conduit.
 12. The computer-implemented method of claim 10,wherein the second conduit comprises a top section and a bottom section,wherein a width of the top section is larger than a width of the bottomsection.
 13. The computer-implemented method of claim 10, wherein thesecond conduit is formed by two notches of the one or moreintraoperative tracking modules, wherein the two notches are adapted toattach to a horizontal notch of the one or more vertebral screws.
 14. Acomputer-implemented method of predicting a surgical outcome a spinalsurgery of a subject, the method comprising: inputting, into a computersystem, one or more preoperative inputs relating to the subject, whereinthe one or more preoperative inputs comprise one or more preoperativemedical images of a spine of the subject and one or more preoperativenon-imaging data inputs of the subject; determining, using the computersystem, one or more measurements from the inputted one or morepreoperative medical images of the spine of the subject, wherein the oneor more measurements comprise a position of one or more vertebrae of thespine of the subject; determining, using the computer system, one ormore preoperative spinopelvic parameters based at least in part on theone or more determined measurements, wherein the one or morepreoperative spinopelvic parameters comprise one or more of lumbarlordosis (LL), preoperative thoracic kyphosis (TK), pelvic incidence(PI), pelvic tilt (PT), or sagittal vertical axis (SVA) for one or morevertebrae; transforming, using the computer system, the determined oneor more preoperative spinopelvic parameters to obtain one or morepreoperative spinopelvic parameters in a frequency domain, wherein thetransforming comprises applying a Fourier transformation to thedetermined one or more preoperative spinopelvic parameters; filtering,using the computer system, the one or more preoperative spinopelvicparameters in the frequency domain, wherein the filtering comprisesfiltering out one or more of the one or more preoperative spinopelvicparameters in the frequency domain comprising a frequency level above apredetermined threshold; applying, using the computer system, one ormore predictive models to generate a predicted surgical outcome in thefrequency domain based at least in part on the filtered one or morepreoperative spinopelvic parameters in the frequency domain and the oneor more preoperative non-imaging data inputs of the subject; andtransforming, using the computer system, the generated predictedsurgical outcome in the frequency domain to obtain a generatedpredictive surgical outcome in a spatial domain, wherein thetransforming the generated predicted surgical outcome in the frequencydomain comprises applying an inverse Fourier transformation to thegenerated predicted surgical outcome in the frequency domain, whereinthe computer system comprises a computer processor and an electronicstorage medium.
 15. The computer-implemented method of claim 4, whereinthe one or more predictive models comprises one or more of a generativeadversarial network (GAN) algorithm, convolutional neural network (CNN)algorithm, or recurrent neural network (RNN) algorithm.
 16. Thecomputer-implemented method of claim 4, wherein the one or moremeasurements from the inputted one or more preoperative medical imagesof the spine of the subject are determined automatically by the computersystem.
 17. The computer-implemented method of claim 4, wherein theinputted one or more preoperative medical images of the spine of thesubject comprises one or more sagittal x-ray images and one or morefrontal x-ray images.
 18. The computer-implemented method of claim 4,wherein the generated predictive surgical outcome in the spatial domaincomprises one or more of one or more postoperative spinopelvicparameters or one or more specifications of a spinal rod to be implantedto the spine of the subject.
 19. The computer-implemented method ofclaim 4, wherein the one or more preoperative inputs further compriseone or more specifications of a spinal rod proposed to be implanted tothe spine of the subject.
 20. The computer-implemented method of claim4, further comprising, generating, by the computer system, apreoperatively determined spinal surgical plan for the subject based atleast in part on the generated predictive surgical outcome in thespatial domain.
 21. The computer-implemented method of claim 20, whereinthe generated preoperatively determined spinal surgical plan comprisesone or more specifications of a spinal rod for implantation during thespinal surgery of the subject.
 22. A computer-implemented method oftraining a predictive model for predicting a surgical outcome a spinalsurgery of a subject, the method comprising: inputting, into a computersystem, one or more preoperative inputs and one or more postoperativeinputs relating to one or more previous subjects, wherein each of theone or more preoperative inputs and the one or more postoperative inputsrelating to one or more previous subjects comprise one or morepreoperative medical images and one or more postoperative medical imagesof a spine of the one or more previous subjects and one or morepreoperative non-imaging data inputs and one or more postoperativenon-imaging data inputs of the one or more previous subjects;determining, using the computer system, one or more measurements fromthe inputted one or more preoperative medical images and one or morepostoperative medical images of the spine of the one or more previoussubjects, wherein the one or more measurements comprise a position ofone or more vertebrae of the spine of the one or more previous subjects;determining, using the computer system, one or more preoperativespinopelvic parameters and one or more postoperative spinopelvicparameters of the spine of the one or more previous subjects based atleast in part on the one or more determined measurements, wherein theone or more preoperative spinopelvic parameters and the one or morepostoperative spinopelvic parameters of the one or more previoussubjects comprise one or more of lumbar lordosis (LL), preoperativethoracic kyphosis (TK), pelvic incidence (PI), pelvic tilt (PT), orsagittal vertical axis (SVA) for one or more vertebrae; applying, usingthe computer system, a data compression technique to the determined oneor more preoperative spinopelvic parameters and the one or morepostoperative spinopelvic parameters of the one or more previoussubjects to obtain compressed one or more preoperative spinopelvicparameters and one or more postoperative spinopelvic parameters of theone or more previous subjects; filtering, using the computer system, thecompressed one or more preoperative spinopelvic parameters and the oneor more postoperative spinopelvic parameters of the one or more previoussubjects, wherein the filtering comprises filtering out one or morecompressed preoperative spinopelvic parameters and one or morepostoperative spinopelvic parameters of the one or more previoussubjects comprising a noise level above a predetermined threshold;training, using the computer system, one or more predictive models basedat least in part on the filtered compressed one or more preoperativespinopelvic parameters and one or more postoperative spinopelvicparameters of the one or more previous subjects, the one or morepreoperative non-imaging data inputs of the one or more previoussubjects, and the one or more postoperative non-imaging data inputs ofthe one or more previous subjects; and testing, using the computersystem, the trained one or more predicted models on one or more testpreoperative inputs and one or more test postoperative inputs relatingto one or more test subjects, wherein each of the one or more testpreoperative inputs and the one or more test postoperative inputsrelating to one or more test subjects comprise one or more testpreoperative medical images and one or more test postoperative medicalimages of a spine of the one or more test subjects, wherein the one ormore test subjects are separate from the one or more previous subjects,wherein the trained and tested one or more predictive models areconfigured to predict the surgical outcome of the spinal surgery of thesubject based at least in part on one or more spinopelvic parametersderived from one or more preoperative medical images of a spine of thesubject, wherein the computer system comprises a computer processor andan electronic storage medium.
 23. The computer-implemented method ofclaim 22, wherein the data compression technique comprises a Fouriertransformation.
 24. The computer-implemented method of claim 22, whereinthe training of the one or more predictive models is based at least inpart on one or more of a generative adversarial network (GAN) algorithm,convolutional neural network (CNN) algorithm, or recurrent neuralnetwork (RNN) algorithm.
 25. The computer-implemented method of claim22, further comprising generating, using the computer system, one ormore augmented measurements by applying a Gaussian process to thedetermined one or more measurements from the inputted one or morepreoperative medical images of the spine of the previous subjects,wherein the generated one or more augmented measurements are configuredto be used to train the one or more predictive models.
 26. Thecomputer-implemented method of claim 22, further comprising generating,using the computer system, one or more augmented measurements fromrotating the one or more preoperative medical images and the one or morepostoperative medical images of the spine of the one or more previoussubjects along a vertical axis, wherein the generated one or moreaugmented measurements are configured to be used to train the one ormore predictive models.
 27. The computer-implemented method of claim 22,wherein the one or more postoperative inputs relating to one or moreprevious subjects further comprise one or more specifications of aspinal rod implanted to the spine of the one or more previous subjects.28. The computer-implemented method of claim 22, wherein the datacompression technique comprises a polynomial function.
 29. Thecomputer-implemented method of claim 28, wherein the one or morepreoperative medical images and the one or more postoperative medicalimages of the spine of the one or more previous subjects are rotatedalong the vertical axis in 180 degrees.